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The burden of cardiovascular disease continues to take its toll in terms of diminished quality of life, reduced life expectancy, and the direct and indirect medical costs of treating and caring for patients. This is in spite of downward trends in mortality in the US and Western Europe, especially in the two decades leading up to the year 2000.
by Bernard Cook, PhD
This decline could, in part, be attributed to the success of patient educational campaigns to reduce smoking, lower levels of cholesterol and blood pressure, and encourage lifestyle changes in exercise and diet. Alongside the wider use of effective medications such as cholesterol-lowering statins, these steps are said to have contributed to an observed decline in coronary heart disease (CHD) deaths [1].
However, the rate of improvement is being affected by other, conflicting trends, such as increasing extreme obesity, sedentary lifestyles, the prevalence of hypertension and the rise in type 2 diabetes mellitus even in children.
The World Health Organization first defined myocardial infarction (MI) in 1979 based on clinical presentation, ECG results and elevated blood enzymes such as total CK and the CK-MB isoform. However, there were no standardized definitions, with clinicians defining MI differently, even within the same hospital. The arrival of cardiac troponin assays in the 1990s opened the door for a much needed reappraisal. It was discovered that a greater sensitivity and specificity for MI diagnosis could be achieved by using troponin (a more cardiac-specific marker than CK-MB) and lower cut-offs, presenting the possibility of using the rise and fall of troponin levels to establish ‘early rule-out protocols’.
Highly sensitive troponin is biomarker of choice
Performance expectations have risen as the sensitivity of troponin assays has improved. Nowadays, the modern, highly sensitive troponin assay has become the gold standard biomarker for the early diagnosis of an acute MI, with standards benchmarked by recent international guidelines.
The first steps towards international conformity were taken in 2000 when the European Society of Cardiology (ESC) and the American College of Cardiology (ACC) collaborated on a redefinition of MI. For the first time this gave a central role to the use of biomarkers such as troponin [2]. By 2007, this collaboration had expanded to include the American Heart Association (AHA) and the World Heart Federation (WHF) and saw the publication of the first universal definition of MI. This further established troponin as the preferred biomarker when diagnosing a heart attack [3].
Five years later, in 2012, the Third Universal Definition of Myocardial Infarction [4] laid down the criteria for the contemporary use of troponin assays by today’s clinicians to reduce the time it takes to rule out acute myocardial infarction (AMI). Crucially, this defined an increased cTn concentration as a value exceeding the 99th percentile of a normal reference population apparently free from heart disease [5, 6].
Establishing upper reference limits
The current definition states that the 99th percentile limit should be determined using a healthy population [5, 7]. It is acceptable to confirm this cut-off from information published in peer-reviewed literature or in the assay manufacturer’s own product literature. Further, troponin assays should demonstrate optimal precision with a coefficient of variation of 10% or less at the 99th percentile value and high sensitivity assays should be able to detect troponin in at least 50% of the reference population [7, 8, 9]. Troponin assays with an imprecision greater than 20% CV at the 99th percentile do not fit the criteria for contemporary use.
In today’s current clinical practice, when patients present to an emergency department with chest pain and acute coronary syndrome is suspected, the requirement for the highly sensitive troponin (Tn) assays, such as Beckman Coulter’s AccuTnI+3 assay, is to rule out non-ST-segment-elevation myocardial infarction (NSTEMI) as quickly as possible. Further, distinguishing acute from chronic c-Tn elevations requires serial measurements to detect significant changes [5].
There are conflicting positions about how to best establish a 99th percentile upper reference limit (URL) for troponin. The first maintains that the URL study should include younger subjects, and should not include subjects with potential cardiovascular disease or cardiac risk factors. The second contends the study should include subjects from a population that represents the intended use for troponin: patients whose demographics reflect those of subjects presenting to the emergency department, including older individuals without known cardiovascular disease. Additionally, other methods for selecting a cardiac-healthy population have been employed, such as samples collected from apparently healthy blood donors.
In a study by Moretti et al to establish that the AccuTnI +3 assay demonstrates a coefficient of variation of 10%, the authors found that 62% of the apparently 330 healthy group of blood donors used in the study had measurable values of troponin between the Limit of Detection (LoD) and 99th percentile values [10]. There were no significant differences related to gender and no correlation between cTnI and age.
Predictive accuracy at early observation times
Storrow et al conducted a multicentre, prospective study, involving more than 1,900 patients, to investigate and compare clinical performance at pre-defined serial sampling intervals: on admission/at 1-3 hours/3-6 hours and 6-9 hours. Patients selected from 14 centres were those presenting with chest pain or equivalent ischemic symptoms suggestive of acute coronary syndromes [11]. Results from this study reinforced current clinical practice that troponin testing provides a high degree of accuracy at early observation times, on admission and three hours later, only needing to be repeated after six hours when clinical suspicion remains high.
The findings, published late 2014 in Clinical Biochemistry, compared emergency department TnI serial sampling intervals to determine optimal diagnostic thresholds, and reported on representative diagnostic performance characteristics for early rule-in and rule-out of MI [11]. Diagnosis was adjudicated by an independent central committee of cardiologists. Study samples were tested using Beckman Coulter’s AccuTnI+3 assay at four independent testing facilities.
Specific results from Storrow showed that TnI ≥0.03 ng/mL provided 96.0% sensitivity and 89.4% specificity at 1-3 hours after admission, and 94.9% sensitivity and 86.7% specificity at 3–6 hours. When troponin levels were <0.03 ng/mL, being able to give a negative predictive value (NPV) depended on knowing the time symptoms started. If it was determined that symptoms started approximately eight hours before admission or examination, the NPV was 99.1%. Testing at 1-3 hours gave a NPV of 99.5%; and 99.0% at 3-6 hours when TnI is >0.03 ng/mL. However, Storrow noted that 50–58% of patients with troponin levels of ≥0.03 ng/mL were diagnosed with MI, depending on the time symptoms started or admission.
Positive predictive values emphasize the importance of taking serial samples and observing rising or falling patterns of the delta TnI when low cut-offs are used. Storrow noted that even a single elevated TnI value increased the risk of MI. As TnI values rose, the probability of MI increased, with values ≥0.20 ng/mL associated with an almost 90% probability [11].
Importance of establishing absolute delta values
A change in serial troponin values (delta) can be reported as a percentage or absolute concentrations between the repeat measurements. However, serial measurements must be calculated with values from the same cTn assay [12]. The larger the value set for the delta, the higher the specificity (and the lower the sensitivity) for acute cardiac injury including AMI [13], and the smaller the value set, the higher the sensitivity.
In a separate report using the AccuTnI+3 assay, Storrow confirmed that absolute delta performed significantly better than relative delta at each time interval; for example, at 1-3 hours (AUC were 0.84 vs 0.69), 3-6 hours (0.85 vs 0.73), and 6-9 hours (0.91 vs 0.79) [14]. Current recommendations propose ≥20% delta within 3-6 hours; however, in this study, results were optimized using an absolute delta of 0.01 or 0.02 ng/mL.
Being able to determine the degree of serial change in high sensitive troponin assay concentrations seems to be the most accurate way of differentiating between those patients suffering AMI and those with more chronic heart conditions. Currently, there is no official consensus on a way of establishing or confirming delta values. Until this is in place, the US Task Force in Clinical Applications of Cardiac Bio-markers recommends that institutions agree on a delta value based on available peer-reviewed data for individual assays and then modify based on empirical findings and feedback [15].
Another study of 874 patients, published in the International Journal of Cardiology, used the Beckman Coulter AccuTnI assay to demonstrate that an algorithm incorporating cTnI concentration and delta cTn values with this assay could allow accurate diagnosis of AMI within two hours from presentation and an earlier rule-out of AMI in the majority of patients.
Cullen et al assessed the accuracy of delta cTn at two and six hours compared to the cTn concentration above the 99th percentile reference value for AMI in a prospective study of adult patients presenting with symptoms suggestive of possible acute coronary syndrome [16]. The area under the ROC curve for diagnosing AMI at two hours was 0.89 [95%CI, 0.84–0.95] for absolute delta cTn versus 0.79 [95%CI 0.73–0.85] for the relative change. Specificity and PPV at two hours were optimized using a delta cTnI ≥0.03 μg/L (95.8% [95%CI 94.1–97.0] and 61.4% [95%CI 50.9–70.9] respectively). Sensitivity and NPV for AMI were optimized using the 99th percentile with the addition of a delta of 0.03 μg/L (97.1% [95%CI 90.2–99.2] and 99.7% [95%CI 99–99.9] respectively).
Labs to adopt lower high sensitive assay cut-offs
With the newer highly sensitive troponin assays, the resulting shift to lower cTn cutoffs will increase the number of patients that are monitored for MI, and will also identify patients with elevated cTn due to other conditions. Published guidance now recommends that all manufacturers demonstrate the true clinical performance of their cTn assays in the contemporary clinical setting through an appropriately designed clinical study [17]. Out-of-date clinical cut-offs and diagnostic criteria may not accurately diagnose MI in some instances. It may take time for laboratories to adopt lower cut-offs, but doing so will ultimately improve patient care [18].
References
1. Ford ES, Capewell S. Coronary heart disease mortality among young adults in the U.S. from 1980 through 2002: concealed leveling of mortality rates. J Am Coll Cardiol. 2007 Nov 27; 50(22):2128-32. Epub 2007 Nov 13.
2. Myocardial Infarction Redefined – a consensus document of the Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. Eur. Heart J 2000; 21: 1502-1513. J Am Coll Cardiol 2000; 36: 959-969.
3. Thygesen K, Alpert JS, White HD; Joint ESC/ACC/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of myocardial infarction. Eur Heart J 2007; 28: 2525-38.
4. Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction. Third universal definition of myocardial infarction. European Heart Journal 2012; 33: 2551-2567. Available online at: http://www.escardio.org/guidelines.
5. Thygesen K, Alpert JS, Jaffe AS, et al. Third universal definition of myocardial infarction. Eur Heart J 2012; 33: 2251–2267.
6. Morrow DA, Cannon CP, Jesse RL, et al. National Academy of Clinical Biochemistry laboratory medicine practice guidelines: clinical characteristics and utilization of biochemical markers in acute coronary syndromes. Clin Chem 2007; 53: 552–574.
7. Collinson PO, Heung YM, Gaze D, Boa F, Senior R, Christenson R, et al. Influence of population selection on the 99th percentile reference value for cardiac troponin assays. Clin Chem 2012; 58:219-25.
8. Apple FS, Collinson PO, and for the IFCC Task Force on Clinical Applications of Cardiac Biomarkers: analytical characteristics of high-sensitivity cardiac troponin assays. Clin Chem 2012; 58:54-61.
9. Apple FS. Ler R, Murakami MM. Determination of 19 cardiac troponin I and T assay 99th percentile values from a common, presumably healthy, population. Clin Chem 2012; 58:1574-81.
10. Moretti M et al. Analytical performance and clinical decision limit of a new release for cardiac troponin I assay. Ann Clin Biochem, April 7, 2014.
11. Storrow AB, et al. Diagnostic performance of cardiac Troponin I for early rule-in and rule-out of acute myocardial infarction: Results of a prospective multicenter trial. Clinical Biochemistry 2014; e-pub ahead of print.
12. Keller T, Zeller T, Ojeda F, Tzikas S, Lillpopp L, Sinning C, et al. Serial changes in highly sensitive troponin I assay and early diagnosis of myocardial infarction. JAMA 2011; 306:2684-93.
13. Korley FK, Jaffe ASJ. Preparing the United States for high-sensitivity cardiac troponin assays. J Am Coll Cardiol 2013; 61:1753-8.
14. Storrow AB, et al. Absolute and relative changes (delta) in troponin I for early diagnosis of myocardial infarction: Results of a prospective multicentre trial. Clin Biochem (2014), http://dx.doi.org/10.1016/j.clinbiochem.2014.09.012.
15. Task Force On Clinical Applications of Cardiac Bio-Markers. Using High Sensitivity Cardiac Troponin Assays in Practice. The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). 2014. http://www.ifcc.org/media/259738/201405_TF_CB_IFCC_practice%20Summary.pdf.
16. Cullen L, Parsonage WA, Greenslade J, Lamanna A, Hammett CJ, Than M, et al. Delta troponin for the early diagnosis of AMI in emergency patients with chest pain. Int J Cardiol 2013;168:2602–8.
17. Letter to Manufacturers of Troponin Assays Listed with the FDA. Available online at: http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/InVitroDiagnostics/ucm230118.htm.
18. Mills NL, Lee KK, McAllister DA, Churchhouse AMD, MacLeod M, Stoddart M, Walker S, Denvir MA, Fox KAA, Newby DE. Implications of lowering threshold of plasma troponin concentration in diagnosis of myocardial infarction: cohort study. BMJ 2012; 344: e1533.
The author
Dr Bernard Cook is Senior Scientific Manager, Beckman Coulter Diagnostics. He has co-authored several scientific papers and is actively involved in the diagnostics industry, which includes being the former chairman of the industry division of the American Association for Clinical Chemistry.
Recently, low-molecular-weight-peptide enrichment from blood samples by on-chip fractionation with nanopore platforms has been established successfully for the quantification and phenotypic characterization of the substrate degradome – the peptide products generated by the protease activity of a tumour environment. This article will provide evidence for this peptidomics-based approach and the clinical relevance in future therapeutic benefits will also be discussed.
by Dr Xu Qian and Dr Tony Y. Hu
Introduction
The development of cancer is a multistep process involving initiation, progression, local-regional recurrence, tumour metastasis and the host anti-tumour response. We are also now aware that changes in the broad genetic and epigenetic landscape as well as molecular mechanisms beyond histology and clinical characteristics contribute to this process. One such mechanism is the relationship between the repertoire of proteases expressed by a tissue and their substrates, which was found to be important in all steps of tumour progression by interactions with tumour cells and the tumour milieu.
Considering the systematic role of proteases in malignant tumour development, it was thought that it might be possible to detect signature products of substrate proteolysis – the substrate degradome – in the patient’s blood samples that are the result of protease dysregulation. This might then function as a diagnostic marker for tumour progression and a surrogate marker for monitoring the effects of protease-inhibitor therapy. This approach, called ‘exogenous peptidomics’ [1], based on mass spectrometry (MS) has proven its usefulness in the discovery of peptides from biofluids.
Challenges remain in this field as a consequence of the low molecular weight, low concentrations and quick degradation of such peptides in the peripheral blood of cancer patients. We recently developed an MS-based on-chip fractionation method assisted by nanopore technology, which has the advantages of being simple, high-throughput, high-resolution, and non-invasive [2]. We successfully identified circulating carboxypeptidase N (CPN)-catalysed C3f-fragments in a breast cancer mouse model as well as in patients with breast cancer [3] and matrix metalloproteinases (MMP)-9-catalysed C3f-fragments in an ovarian cancer mouse model [4]. This review discusses the applications of this new approach for studying peptide profiling in relation to tumour-resident proteases as biomarkers and potential therapy target.
Proteases and the substrate degradome in cancer development
The developing tumour microenvironment is composed of proliferating tumour cells, blood vessels, infiltrating inflammatory cells, a variety of associated tissue cells and tumour stroma, as well as secreted cytokines, chemokines, growth factors and matrix-degrading proteases. Intracellular and extracellular proteases that can function as signalling molecules play an indispensable role in this neoplastic process by enhancing cell proliferation, survival, adhesion, migration, angiogenesis, senescence, autophagy, apoptosis and evasion of the immune system in the tumour microenvironment [5–7]. For example, intracellular granzyme B is a well-known protease facilitating the ability of NK cells and CD8+ T-cells to kill their targets in the tumour milieu [8]. Elevated levels of granzyme B were also found in pre-metastatic niches presenting a novel role for the activation of CD8+ T-cells in constraining myeloid cell activity through direct killing [9]. Interestingly, recent publications suggested that granzyme B has a double-edged function. Regulatory T (Treg)-cells derived from the tumour environment may induce NK and CD8+ T-cell death in a granzyme B- and perforin-dependent fashion [10, 11] or kill CD4+ effector T-cells via granzyme B in the presence of IL-2 [12]. These findings indicate that granzyme B is relevant for Treg-cell-mediated suppression of tumour clearance in vivo.
Extracellular matrix (ECM) degradation by proteolysis is critical for tumour invasion and metastasis. Many proteases such as matrix metalloproteinases (MMPs), cathepsin and the urokinase-type plasminogen activator system play roles in the degradation of ECM in tumour progression [13, 14]. Extracellular granzyme B released from migrating cytotoxic lymphocytes was found to participate in the remodelling of vascular basement membranes (BMs) by cleaving BM constituents and enabling chemokine-driven movement through BMs in vitro [15]. Recently, another granzyme family member, granzyme M, was reported to be an inducer of epithelial-mesenchymal transition (EMT) in cancers associated with STAT3 activation [16]. Cancer cells with EMT features were capable of changing their shape, polarity and motility in a malignant manner. In the same study, overexpression of granzyme M in cancer cells was found to promote chemoresistance. The EMT phenotype of cancer cells was also achieved by increased MMP-9 production and MMP-9-mediated degradation of E-cadherin, involving ERK1/2 pathways [17].
In elucidating the role of proteases in cancer development, it is also important to gain a better understanding of the substrate degradome, which consists of the terminal peptide products of the activities of the multistage proteases. We have, therefore, identified hundreds of substrates of granzyme B, affecting cell lysis, receptors, cytokines and growth factors, as well as extracellular-matrix-structural proteins and intracellular proteins involved in cell signalling and cycle regulation [15, 18]. Besides granzyme B, MMPs cleave an increasingly large set of substrates, such as elastin, fibronectin, laminin and collagen IV [14, 19, 20]. However, given the broad range of substrate function, the mechanism of protease temporal and spatial regulation remains largely unknown. The exact role of proteases and substrates in cancer biology both at tissue level and in circulation is still needs to be clearly defined.
A new tool for the detection of circulating tumour-associated peptides as biomarkers
The proteolytic products of a protease are a useful indicator of the protease concentration in serum. This is necessary, in part, because of the low concentration of protease itself in serum compared with high detection limits, the quick degradation of the protease, and the inaccuracy of detection methodology such as enzyme linked immunosorbent assay (ELISA) as a result of irreversible non-specificity. New protocols are currently being developed for the detection and validation of substrate/peptides via peptidomics [21, 22].
Our group developed a platform mainly including peptide on-chip fractionation followed by a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis [23]. Briefly, nanoporous silica (NPS) thin films with nanotextures are used to capture and preserve low-molecular-weight peptides from 5 μl serum or plasma samples, whereas high-molecular-weight proteins are excluded by a wash step (Fig. 1). By this on-chip fractionation, low-molecular-weight peptides are enriched and separated. The peptides appear as m/z peaks that could be detected by MALDI-TOF MS analysis. The main advantage of this platform is that it allows specific profiling of the peptidome with high-throughput, high-resolution and a simple loading step.
Using this unique platform, we tested one hypothesis that, in breast cancer, CPN activity and its proteolytic products could be detected in interstitial fluid and blood [3]. CPN together with its substrate/peptide product levels may vary during tumour initiation and progression, indicating different disease states. We confirmed by ex vivo peptide cleavage assay and in vivo validation that the previously identified substrate C3f_S1304-R1320 was cleaved by CPN specifically at the C-terminal arginine. Moreover, six fragments generated from C3f_S1304-R1320 cleavage by CPN increased significantly in mouse sera at 2 weeks after orthotopic implantation relative to normal controls. The most important finding, however, documented that the plasma levels of substrate/peptide products of CPN were apparently elevated in patients with early stage breast cancer relative to controls, but levels of CPN protein itself were unchanged. These observations indicate that there may be additional regulation of CPN at different stages of tumour development. It is likely that inhibition of CPN protease activity is included within this additional regulation. However, the presence and frequency of substrate/peptides of CPN in the early stage of breast cancer makes them potential biomarkers for early diagnosis.
Recently, we investigated MMP-9 activity with the aim of monitoring a novel therapeutic strategy. To do this we used a HeyA8-MDR-induced ovarian cancer mouse model, where HeyA8-MDR cells are a human drug-resistant ovarian cancer cell line [4]. This study provided two major observations: (1) C3f was cleaved by MMP-9 in the tumour microenvironment. Two fragments generated specifically by this proteolysis were released and were detectable in mouse serum. (2) Treatment with ephrin type-A receptor 2-siRNA-multistage vectors (MSV-EphA2) induced apoptosis of tumour cells and a down-regulation of MMP-9 in tumour tissue. Moreover, the decreased level of circulating C3f cleavage fragments correlated with MSV-EphA2 treatment. Therefore, this change could be tracked and used to monitor treatment efficiency in real-time by a simple on-chip blood test. Taken together, these data suggest that the effect of EphA2 treatment extends to the peripheral blood, well beyond the tumour microenvironment at the tissue level, and thus can be easily assessed.
We reasoned that, from these two experimental approaches, it might be possible to gain information about the dynamic processes of proteases and their substrate/peptide products in patients with cancer. Consequently, further research in this field combined with other investigations aimed at improving the management of patients with cancer by early diagnosis, accurate characterization of disease, focused, treatment efficiency, and prognosis is essential.
Conclusion
In summary, MS-based on-chip fractionation assisted by nanopore platforms has been shown to be a highly sensitive and practical tool for the quantification and characterization of the circulating degradome. The combination of cellular protease function as well as substrate/peptide analysis provides a biologically meaningful picture of a specific tumour-entity at the level of the single peptide. Analysis of the circulating pepidome will be complementary to the standard diagnostic or prognostic procedure, such as routine blood test and tissue biopsy, for patients with cancer. This approach also holds great promise as a tool for monitoring novel therapeutic targets. For further development of this technique, many predicted targets still await validation as direct protease substrates and clarification of biological relevance in the network of protease and its inhibitors. We should pay much attention to the potential pitfalls.
References
1. Peccerella T, Lukan N, Hofheinz R, Schadendorf D, Kostrezewa M, Neumaier M, Findeisen P. Endoprotease profiling with double-tagged peptide substrates: a new diagnostic approach in oncology. Clin chem. 2010; 56(2): 272–280.
2. Fan J, Niu S, Dong A, Shi J, Wu HJ, Fine DH, et al. Nanopore film based enrichment and quantification of low abundance hepcidin from human bodily fluids. Nanomedicine. 2014; 10(5): 879–888.
3. Li Y, Li Y, Chen T, Kuklina AS, Bernard P, Esteva FJ, et al. Circulating proteolytic products of carboxypeptidase N for early detection of breast cancer. Clin Chem. 2014; 60(1): 233–242.
4. Deng Z, Li Y, Fan J, Wang G, Li Y, Zhang Y, et al. Circulating peptidome to indicate the tumor-resident proteolysis. Sci Rep. 2015; 5: 9327.
5. D’Eliseo D, Pisu P, Romano C, Tubaro A, De Nunzio C, Morrone S, et al. Granzyme B is expressed in urothelial carcinoma and promotes cancer cell invasion. Int J Cancer 2010; 127(6): 1283–1294.
6. Hu SX, Wang S, Wang JP, Mills GB, Zhou Y, Xu HJ. Expression of endogenous granzyme B in a subset of human primary breast carcinomas. Br J Cancer 2003; 89(1): 135–139.
7. Jezierska A, Motyl T. Matrix metalloproteinase-2 involvement in breast cancer progression: a mini-review. Med Sci Monit. 2009; 15(2): RA32–40.
8. Keefe D, Shi L, Feske S, Massol R, Navarro F, Kirchhausen T, et al. Perforin triggers a plasma membrane-repair response that facilitates CTL induction of apoptosis. Immunity 2005; 23(3): 249–262.
9. Zhang W, Zhang C, Li W, Deng J, Herrmann A, Priceman SJ, et al. CD8+ T-cell immunosurveillance constrains lymphoid premetastatic myeloid cell accumulation. Eur J Immunol. 2015; 45(1): 71–81.
10. Gondek DC, Lu LF, Quezada SA, Sakaguchi S, Noelle RJ. Cutting edge: contact-mediated suppression by CD4+CD25+ regulatory cells involves a granzyme B-dependent, perforin-independent mechanism. J Immunol. 2005; 174(4): 1783–1786.
11. Cao X, Cai SF, Fehniger TA, Song J, Collins LI, et al. Granzyme B and perforin are important for regulatory T cell-mediated suppression of tumor clearance. Immunity 2007; 27(4): 635–646.
12. Strauss L, Bergmann C, Whiteside TL. Human circulating CD4+CD25highFoxp3+ regulatory T cells kill autologous CD8+ but not CD4+ responder cells by Fas-mediated apoptosis. J Immunol. 2009; 182(3): 1469–1480.
13. Parks WC, Wilson CL, Lopez-Boado YS. Matrix metalloproteinases as modulators of inflammation and innate immunity. Nat Rev Immunol. 2004; 4(8): 617–629.
14. Ota I, Li XY, Hu Y, Weiss SJ. Induction of a MT1-MMP and MT2-MMP-dependent basement membrane transmigration program in cancer cells by Snail1. Proc Natl Acad Sci U S A. 2009; 106(48): 20318–2023.
15. Prakash MD, Munoz MA, Jain R, Tong PL, Koskinen A, Regner M, et al. Granzyme B promotes cytotoxic lymphocyte transmigration via basement membrane remodeling. Immunity 2014; 41(6): 960–972.
16. Wang H, Sun Q, Wu Y, Wang L, Zhou C, Ma W, et al. Granzyme M expressed by tumor cells promotes chemoresistance and EMT in vitro and metastasis in vivo associated with STAT3 activation. Oncotarget. 2015; 6(8): 5818–5831.
17. Zuo JH, Zhu W, Li MY, Li XH, Yi H, Zeng GQ, et al. Activation of EGFR promotes squamous carcinoma SCC10A cell migration and invasion via inducing EMT-like phenotype change and MMP-9-mediated degradation of E-cadherin. J Cell Biochem. 2011; 112(9): 2508–2517.
18. Boivin WA, Cooper DM, Hiebert PR, Granville DJ. Intracellular versus extracellular granzyme B in immunity and disease: challenging the dogma. Lab Invest. 2009; 89(11): 1195–1220.
19. Martinez A, Oh HR, Unsworth EJ, Bregonzio C, Saavedra JM, Stetler-Stevenson WG, et al. Matrix metalloproteinase-2 cleavage of adrenomedullin produces a vasoconstrictor out of a vasodilator. Biochem J. 2004; 383(Pt. 3): 413–418.
20. Wang S, Dangerfield JP, Young RE, Nourshargh S. PECAM-1, alpha6 integrins and neutrophil elastase cooperate in mediating neutrophil transmigration. J Cell Sci. 2005; 118(Pt 9): 2067–2076.
21. Kwong GA, von Maltzahn G, Murugappan G, Abudayyeh O, Mo S, Papayannopoulos IA, et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat Biotech. 2013; 31(1): 63–70.
22. Ueda K, Saichi N, Takami S, Kang D, Toyama A, Daigo Y, et al. A comprehensive peptidome profiling technology for the identification of early detection biomarkers for lung adenocarcinoma. PLoS One. 2011; 6(4): e18567.
23. Hu Y, Peng Y, Lin K, Shen H, Brousseau LC, 3rd, Sakamoto J, et al. Surface engineering on mesoporous silica chips for enriching low molecular weight phosphorylated proteins. Nanoscale 2011; 3(2): 421–428.
The authors
Xu Qian MD1,2, Tony Y. Hu PhD*1,3
1Dept of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030, USA
2Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical University, Zhejiang, PR China
3Dept of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, NY 10065, USA
*Corresponding author
E-mail: yhu@houstonmethodist.org
Two opposing agendas confront clinical labs in terms of electronic health records (EHRs): privacy/security on the one side, and interoperability, on the other. The former involves an inward push for isolation, while the latter tends to pull technology in the other direction.
There also is a major financial challenge. While healthcare providers have been given a host of incentives to adopt EHRs (especially in the US), labs have been pretty much left out on their own.
EHRs and lab systems populate different worlds
Clearly, lab-compatible EHR systems which meet both (privacy and interoperability) criteria promise the quickest returns. EHR developers have however shown little enthusiasm, until recently, to incorporate clinical lab requirements as a sufficient driver, while laboratory system vendors have tended to ignore EHRs or postpone taking them into account until EHR development has matured sufficiently.
US EHR adoption drives lab applications
In the US, this limbo is being shaken up by healthcare providers, who are compelling vendors to take account of their need for EHR-friendly clinical lab systems.
At end 2012, the US Centers for Disease Control and Prevention (CDC) released a survey which found 72 percent of office-based physicians using EHR systems, up from 48 percent in 2009 and 18 percent in 2001.
The reason for the dramatic increase in EHR adoption lies in the Meaningful Use requirements of the 2009 Health Information Technology for Economic and Clinical Health Act, also known as the HITECH Act. The Act provides billions of dollars in incentive payments through the Medicare and Medicaid programmes to increase physician adoption of EHR systems.
Clinical labs are now being lifted by the rising tide of EHR adoption. According to the US Office of the National Coordinator for Health Information Technology (ONC), the “availability of structured lab results within the EHR contributes to office efficiencies while also assisting providers in the ability to make real time decisions about the patient’s care.”
The ONC explicitly specifies the threshold for EHR-friendly clinical lab practices in Stage 1 – of over 40 percent of all lab test results ordered by a provider and incorporated in certified EHR technology as structured data.
Stage 2 Meaningful Use requirements, finalised in August 2012, increase the clinical lab results threshold to 50 percent. The ONC has subsequently announced plans to assess health information exchange (HIE) in clinical laboratories.
Labs left to own resources
While healthcare providers have the financial incentives of the HITECH Act, clinical labs have been left to their own resources to set up interfaces from their laboratory information systems (LIS) to providers.
Compounding this has been inconsistencies in the way different EHR systems generate lab test orders.
However, the alternative has been stark – to be left out of referrals from tests.
EHR systems remain heterogeneous
The US EHR landscape is however hardly uniform. As of September 2013, there were 3,652 non-enterprise certified ambulatory EHR software systems, almost half of which were classified as “complete” to qualify for Meaningful Use Stage 1 or Stage 2.
In spite of efforts to set standards for semantic interoperability of healthcare data, standards so far are only syntactic (based on HL7 and XML).
The alternative, to develop a common US-wide EHR system, has been accepted as being technically insurmountable – due to hurdles in specifying, developing, testing and deploying standardized tools, common architectures and vocabularies, within secure, real-time and scalable networks, and doing all this within the fast-changing world of information and communications technologies.
For proponents of a decentralized approach to EHR technology, in the US in particular, the sharp increase in offtake of EHR systems has shown that it has delivered – as far as healthcare IT objectives are concerned.
EHR faces teething problems
Still, teething troubles for EHRs also clearly remain.
In early September 2013, one of the leading EHR systems, from EPIC, crashed across seven major healthcare facilities of Sutter Health, a nearly 100 year-old healthcare provider in California. Some suspect the role of a routine upgrade a few days earlier in the EHR system, which was launched by Sutter at a cost of $1.2 billion in 2004, but has so far reached only a halfway mark.
EHR challenges for labs remain to be resolved
Such issues with the evolution of maturity of EHRs pose especially major problems for labs, who (as mentioned) have to develop and fund interfaces between their LISs and the EHRs of their client physicians but are also forced to cope with the lack of uniform EHR standards.
Some vendors have nevertheless sought to fill the gap.
A leading example is HDD Access, a joint initiative by the US Department of Defense, the Department of Veterans Affairs and 3M Health Information Systems to create a public use version of 3M’s Healthcare Data Dictionary (HDD). HDD Access consists of a relational database and Application Programming Interface (API) runtime services to which other applications can interface. The terminology is organized as a controlled medical vocabulary – a comprehensive set of clinical and other concepts used in healthcare.
HDD Access offers specific benefits for integrating LIS and EHR platforms. Independent of source system, it can track local fields and translate them into laboratory concepts. Nevertheless, HDD Access warns that it is “not a standard terminology and is not a replacement for standard terminologies.
In effect, in the US, clinical labs are likely to continue to face a host of technical challenges with respect to EHRs in the years to come.
EHR Big Bang fizzles in Europe
Unlike the US, Europe made a massive effort in 2004 to devise common semantic standards for EHR interoperability as part of its Single eHealth Area. The EU’s EHR objectives sought to integrate all patient information – from primary to tertiary settings, and include emergency and in-patient care. Also on the radar were ambitious plans to connect pharmacies as well as the web of disparate billing/reimbursement procedures, and do so across Europe.
In mid–2008, the EU Commission set 2015 as the target year for EHR interoperability, to ensure that key EHR datasets could cross European borders, and do so in conformity with medical rules and other relevant legal frameworks.
In January 2011, however, these ambitions were put on the backburner, after an official report criticized the effort as being both impractical and ‘grandiose’. The report found that a pan-EU EHR system would neither be technically feasible, cost-effective or even medically justified, and instead urged more emphasis on decentralized efforts – in other words, just like the US.
Technical challenges aside, massive differences in physician and medical cultures across Europe played a major role in derailing efforts toward a common EHR. Or, as EuroRec, an umbrella organization tasked with pan-EU EHR implementation, states: it was “widely recognized that social and organizational aspects are as likely to ruin an implementation process as technical factors are.”
European focus shifts to national efforts
The EHR focus in Europe has now totally shifted to national efforts. A new eHealth Governance Initiative (eHGI) encourages cooperation “between Member States” and “between national authorities and standardization bodies”, and seeks to “enable the recommendation of standards and (harmonized) profiles based on selected use cases.” On the technical side, compared to the Big Bang efforts of the Single eHealth Area, it also aims to “link and harmonize coding systems” and “facilitate access to existing standards and medical vocabularies.”
The second area for Europe’s EHR focus is a minimalistic intra-EU/regional approach embodied in a project called epSOS, which dates back to 2008, but was (temporarily) eclipsed by the ambitions of the Single eHealth Area. epSOS, which went live in April 2012, has the modest goal of connecting 20 EU nations (and 3 non-EU members) to a secure database, and sharing only Patient Summaries and ePrescription records via IHE X* profiles. Its target consists of Europeans holidaying overseas.
Today, EHR adoption varies considerably in Europe. The Nordic countries have been using the technology for over a decade and are fairly advanced as a result in EHR implementation.
However, adoption in France, Germany, Spain and the UK is ‘on course’ with the US.
Shift from Single eHealth Area encourages new EHR-directed lab applications
The shift away from forcing through a Single eHealth Area has also opened the way for innovative working approaches aimed at clinical labs. One good example of this is Valle de los Pedroches Hospital at Cordoba, Spain, which has designed and implemented a unified lab test request module for the Andalusian regional EHR.
In spite of some outstanding issues (such as rigidity in error solving, and the need to adapt to a new nomenclature), implementation of the laboratory module in the EHR improved the analytical process, with better patient safety and less programming or container errors and shorter response times. Clinical professionals gave a rating of 7.8 out of 10, positively highlighting the speed at which results are delivered and their integration in the EHR.
Such efforts are likely to grow with time.
Because of the critical nature of the reported clinical events (such as stent thrombosis and hemorrhage) associated with patients with certain cytochrome p450 2C19 (CYP2C19) variants receiving clopidogrel therapy, additional clinical studies are warranted. Large scale clinical trials seeking to correlate patient’s response to clopidogrel (Plavix®) with the CYP2C19 genotype may benefit from the inclusion of the CYP2C19 genotyping results into therapeutic anti-platelet therapy decisions before percutaneous coronary intervention.
by Dr H. Han, K. Blakely and Dr S. Lewis
Clinical potential of pharmacogenetic testing
The clinical potential of pharmacogenetic testing has increasingly been shown to influence treatment effectiveness for a number of therapies including tyrosine kinase inhibitors (TKIs) such as imatinib directed at chronic myelogenous leukemia (CML), gefitinib and erlotinib targeting the epidermal growth factor receptor (EGFR) in lung and other cancers and clopidogrel, an anti-platelet therapy prescribed for patients receiving percutaneous coronary intervention (PCI) [1–4].
Using genetic information in a clinically beneficial fashion, that is providing evidence-based data to show that the genetic information provided to the health care team is clinically applicable to diagnosis, treatment and prognosis and fiscally responsible, is challenging. Despite numerous studies relating genetic variants to clinical effects, the integration of genetics into routine clinical practice is restricted [5]. Success has been demonstrated especially in the area of oncology and many patients receive cancer therapy guided by genetic testing. Some examples of cancers that have therapy that may be guided by molecular testing include breast, lung, colon and leukemia. Obstacles to integration of genetic testing into routine practice include reimbursement issues and accessibility. Of particular concern to the cardiology team is the past unavailability of rapid point-of-care genotype testing for cytochrome p450 2C19 (CYP2C19) variants during PCI. Molecular genetic testing is typically performed at reference laboratories and the results of the testing have not been available to cardiologists at the time of PCI.
Moving to a state of personalized medicine and genome-guided care requires a number of important steps:
Clopidogrel therapy and genetic testing
The CYP2C19 enzyme is responsible for the metabolism of approximately 15% of all prescription drugs including anti-platelet therapies such as clopidogrel, beta-blockers (propranolol), anti-depressants (imipramine), anti-convulsants (phenytoin) and proton pump inhibitors (omeperizol) [6]. Of particular interest to cardiologists is anti-platelet therapy. Anti-platelet therapy may be classified according to the target of action and includes ADP antagonists (clopidogrel, prasugrel and ticlopidin), COX inhibitors (aspirin), phosphodieterase inhibitors (dipyridamole) and GP IIb/IIIa inhibitors (tirofiban, eptifibatide, abcixmab) shown in Figure 1. Clopidogrel bisulfate is a thienopyridine irreversible inhibitor of ADP-induced platelet aggregation by directly preventing ADP binding to its receptor P2Y12 and thereby preventing subsequent activation of the of the glycoprotein IIb/IIIa complex (Fig. 2). Platelets are irreversibly inactive for the remainder of their life, approximately 7–10 days [6].
Metabolism of clopidogrel (Fig. 3) occurs in the liver by several p450 enzymes. CYP2C19 is involved in the formation of the primary inactive metabolite, 2-oxo-clopidogrel and the active thiol clopidogrel deritive. Anti-platelet effects may be seen 2 hours after oral administration with a steady state inhibition reached between days 3–7. Anti-platelet effects are dose dependent and may be measured by platelet aggregation assays and differ according to CYP2C19 genotype. This association of CYP2C19 genotype and clopidogrel treatment outcome was evaluated in several clinical trials [2, 10].
Variants of the CYP2C19 enzyme include both reduced drug metabolism variants (*2,*3,*4,*5) and increased drug metabolism variants (*17). Most patients undergoing the insertion of a drug eluting stent after myocardial infarction are prescribed clopidogrel bisulfate (Plavix ®) and aspirin as anti-platelet therapy. Numerous investigations of patients prescribed the anti-platelet drug clopidogrel bisulfate (Plavix®) have demonstrated relationships between the patient’s CYP2C19 genotype and their response to clopidogrel as related to clinical outcomes such as blood clots, stent thrombosis, bleeding, myocardial infarctions and major cardiovascular events (MACE). Loss of function CYP2C19*2 and *3 variants have been associated with higher levels of ADP-induced platelet aggregation in patients receiving clopidogrel therapy, and therefore have a greater risk of major cardiovascular events, including stent thrombosis. Several clinical variables are implicated in the platelet response to clopidogrel, but the strongest predictor is the loss of function CYP2C19*2 allele. Approximately 30% of western European individuals, and 50% of Asian individuals carry the *2 allele and studies have associated the *2 allele with a significant increased risk of adverse cardiovascular events and stent thrombosis [11, 12].
In March of 2010, the FDA announced a boxed warning to the clopidogrel label, alerting patients and clinicians that clopidogrel may be less effective in patients carrying the reduced function alleles [13].
The CYP2C19*17 gain-of-function allelic variant was shown to significantly reduce ADP-induced platelet aggregation in clopidogrel-treated patients and was therefore predicted to confer an increased risk of bleeding. CYP2C19*17 alleles are found in the United States population ranging from <5% homozygotes to ~40% heterozygotes. The most common CYP2C10*17 variant, -806C>T, is associated with recruitment of a transcription factor to the mutated site, enhancing transcription and expression of the CYP2C19 enzyme. [14]
Current and future options for CYP2C19 testing
Currently, molecular reference laboratories can provide CYP2C19 genotyping results in several days. Recently, the Spartan RX point-of-care instrument was FDA cleared for in vitro diagnostic testing. This platform uses buccal cells and can provide results in about one hour [15].
Because of the past inability of molecular laboratories to provide cardiologists with rapid point-of-care testing for CYP2C19 variants, large-scale clinical trials with rapid genetic results provided to clinicians at the time of the PCI have been limited. Currently, a clinical trial (NCT01742117) is sponsored by the Center for Individualized Medicine at Mayo Clinic and is entitled ‘Tailored Antiplatelet Initiation to Lessen Outcomes due to Clopidogrel Resistance after Percutaneous Coronary Intervention’ (TAILOR-PCI). This randomized prospective study will use the FDA-cleared Spartan RX to genotype CYP2C19 for the *2,*3 and*17 alleles in cardiac stent patients. The carriers will receive ticagrelor instead of clopidogrel. Approximately 6000 patients will be enrolled in this large study with a completion date of June 2016. Results of this study should provide additional risk stratification and treatment decisions. Additional trials to formulate strategies for the most effective and cost efficient anti-platelet treatment will hopefully follow [15].
Advances in next-generation sequencing (NGS) are providing the promise of available genetic information on patients at rapidly reduced costs. Clinical applications of NGS will hopefully include pharmacogenetic information about patients that may be accessed by clinicians through the electronic medical record to provide immediate guidance of optimum therapy for not only anti-platelet therapy but for numerous other medications.
References
1. Gurbel PA, Tantry US. Controversies in cardiovascular medicine. Platelet function testing and genotyping improve outcome in patients treated with antithrombotic agents. Circulation 2012; 125: 1276–1287.
2. Sibbing D, Koch W, Gebhard D, et al. Cytochrome 2C19*17 allelic variant, platelet aggregation, bleeding events, and stent thrombosis in clopidogrel-treated patients with coronary stent placement. Circulation 2010; 121: 512–518.
3. Krishna V, Diamond GA, Kaiil S. Do platelet function testing and genotyping improve outcome in patients treated with antithrombotic agents?: the role of platelet reactivity and genotype testing in the prevention of atherothrombotic cardiovascular events remains unproven. Circulation 2012; 125: 1288–1303.
4. Mega JL, Close SL, Wiviott SD, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Eng J Med. 2009; 360: 354–362.
5. Roberts JD, Wells GA, Le May ML, et al. Point-of-care genetic testing for personalization of antiplatelet treatment (RAPID GENE): a prospective, randomized, proof-of-concept trial. Lancet 2012; 379: 1705–1711.
6. Plavix package insert. Bristol-Myers Squibb 2009.
7. Tantry U, Kereiakes D, Gurbel P. Clopidogrel and proton pump inhibitors. influence of pharmacological interactions on clinical outcomes and mechanistic explanations. JACC Cardiovasc Interv. 2011; 4: 365–380.
8. Gurbel PA, Tantry US, Shuldiner AR. Letter by Gurbel et al. regarding article, “Cytochrome 2C19*17 allelic variant, platelet aggregation, bleeding events, and stent thrombosis in clopidogrel-treated patients with coronary stent placement”. Circulation 2010; 122: e478.
9. Sibbing D, Koch W, Gebhard D, et al. Response to letter regarding article, “Cytochrome 2C19*17 allelic variant, platelet aggregation, bleeding events, and stent thrombosis in clopidogrel-treated patients with coronary stent placement”. Circulation 2010; 122: e479.
10. Taubert D, Kastrati A, Harlfinger S, et al. Pharmacokinetics of clopidogrel after administration of a high loading dose. Thomb Haemost. 2004; 92: 311–316.
11. Simon T, Verstuyft C, Mary-Krause M, et al. Genetic determinants of response to clopidogrel and cardiovascular events. N Engl J Med. 2009; 360:363–375.
12. Brilakis ES, Patel VG, Banerjee S. Medical management after coronary stent implantation: a review. JAMA 2013; 310: 189–198.
13. Holmes DR Jr, Dehmer GJ, Kaul S, et al. ACCF/AHA Clopidogrel clinical alert: approaches to the FDA “boxed warning”: a report of the American College of Cardiology Foundation Task Force on clinical expert consensus documents and the American Heart Association endorsed by the Society for Cardiovascular Angiography and Interventions and the Society of Thoracic Surgeons. J Am Coll Cardiol. 2010; 56: 321–341.
14. Rudberg I, Mohebi B, Hermann M, et al. Impact of the ultrarapid CYP2C19*17 allele on serum concentration of escitalopram in psychiatric patients. Clin Pharmacol Ther. 2008; 83: 322–327.
15. Spartan Bioscience announces 6,000-patient study of personalized medicine for cardiac stents. PRWeb 2012. (http://www.prweb.com/releases/2012/12/prweb10252073.htm)
The authors
Heping Han MD, PhD, MB (ASCP);
Katherine Blakely BS; Sally Lewis* PhD, MLS (ASCP), MB
Tarleton State University, Fort Worth,
TX 76104, USA
*Corresponding author
E-mail: slewis@tarleton.edu
Effective screening strategies have not yet been developed for the early detection of ovarian cancer. The serum biomarker CA125, routinely used to aid diagnosis and monitor treatment response, is not informative in all patients. Recent analytical developments have prioritized promising candidate novel biomarkers or multi-biomarker panels for future clinical evaluation.
by E. L. Joseph, Dr M. J. Ferguson and Dr G. Smith
Introduction to ovarian cancer
Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy and the fifth leading cause of cancer related death among women with around 140 000 annual deaths worldwide. EOC can develop as one of four histotypes with the serous histotype being the most common and most aggressive. The remaining three non-serous histotypes, endometrioid, clear cell and mucinous cancers present less frequently. High grade serous ovarian cancer, heterogeneous in nature and rapidly progressive, has a poor prognosis, where a major contributing factor is the lack of ability to diagnose the disease at a sufficiently early stage to facilitate curative surgery. The 5-year survival rate is less than 30% for patients presenting with advanced disease spread beyond the ovaries (FIGO Stage 3/4), but if detected earlier combination therapy of cytoreductive surgery and adjuvant or neo-adjuvant chemotherapy with platinum and taxane-based drugs has the potential to cure 90% of patients [1]. Consequently the identification of biomarkers capable of detecting ovarian cancer at the earliest stages and monitoring disease progression are inherently important in tackling this lethal disease. Due to its prevalence, the ideal biomarker for detecting early stage ovarian cancer requires an extremely high specificity (>99%) and a minimum sensitivity of 75% [2]. Despite extensive research, no optimal ovarian cancer biomarker has yet been identified and such high specificity is unlikely to be met by a single agent. Many promising candidate biomarkers are however currently undergoing evaluation in clinical trials.
CA125
The only ovarian cancer biomarker routinely used in the clinic is cancer antigen 125 (CA125; mucin 16) currently considered the ‘gold standard’ cancer biomarker despite its limitations. In the majority of patients with EOC, expression of the CA125 glycoprotein is raised above the normal reference range (>35 U/ml blood), but it only has a sensitivity of 50% to 60% with a specificity of 90% in early stage postmenopausal patients [3]. Several factors, however, limit the utility of CA125 in routine population screening: it is not expressed in 20% of ovarian cancers, is only significantly elevated in 47% of early stage ovarian cancers (although increasing to 80–90% in advanced stage cancers) and can be raised in many benign conditions including endometriosis and peritonitis. Variability in CA125 expression throughout the menstrual cycle and in pregnancy is also a common confounding issue. A major clinical utility of CA125, however, is related to its ability to commonly reflect clinical response following chemotherapy treatment and as such is often successfully used to monitor a patient’s progress through chemotherapy. A reduction in CA125 expression during treatment is considered a positive prognostic outcome for the patient and serial serum measurements are currently used to predict therapeutic outcomes and estimate stability of the disease (Fig. 1).
Biomarkers under evaluation
There have now been multiple attempts to identify novel ovarian cancer biomarkers with varying success. The most promising serum biomarkers include HE4 and mesothelin.
HE4
Human epididymis protein 4 (HE4) has been shown to be consistently elevated above the normal level (151 pM) in ovarian cancers, with sensitivity of 95% and specificity of 73%. HE4 is differentially expressed in specific subtypes of ovarian cancer, potentially allowing clinicians to distinguish histotypes to aid treatment; HE4 was found to be overexpressed in 100% of endometrioid cancers, 93% of serous cancers but only 50% of clear cell cancers [4]. Unlike CA125, it is less likely to produce false positives in benign masses and it has also been proposed to be the best candidate biomarker for early detection of Stage I disease despite sensitivity and specificity of 46% and 95% respectively [2]. HE4 has recently obtained FDA approval in the USA for monitoring recurrence or progression of EOC and, in comparative tests, has been found to be superior to CA125 in classifying benign and borderline ovarian cancers.
Mesothelin
Mesothelin is a glycoprotein expressed by mesothelial cells, the expression of which has been found to be raised in mesothelioma, pancreatic and ovarian cancers.
It can be easily measured in both urine and serum, highlighting its potential as a non-invasive biomarker. Serum mesothelin levels were found to be increased in approximately 60% of ovarian cancers with 98% specificity.
One study found elevation of mesothelin in 42% of urine assays as opposed to 12% serum assays of early stage EOCs at 95% specificity which reinforces the potential of this glycoprotein as an early detection biomarker and the use of urine in preference to serum [2]. Higher levels of mesothelin were also found to be associated with poorer overall survival in patients following optimal debulking surgery or who have advanced stage ovarian cancer. A recent study, however, revealed that lifestyle choices such as smoking and BMI can affect mesothelin levels, which also often increase with age.
Identification of new candidate biomarkers
Due to an urgent need for better biomarkers for early detection of ovarian cancer and reliable biomarkers to monitor clinical response, ongoing efforts are focused on the application of state of the art technologies e.g. mass spectrometry and quantitative proteomic analysis to identify novel biomarkers [5]. These approaches however often generate multiple candidate biomarkers for further investigation, prioritization and clinical evaluation of which is an ongoing challenge. These methods allow comparison of multiplex biomarker panels and identification of novel differentially expressed proteins not previously linked to ovarian cancer.
Another powerful technology is microarray-based mRNA analysis which allows genome wide expression studies which have already enhanced the understanding of the genes and pathways which influence ovarian cancer progression, chemotherapy response and survival. For example, the candidate biomarkers osteopontin and kallikrein (Table 1) were discovered by this method.
Our own studies have revealed significant differences in the expression of fibroblast growth factor 1 (FGF1) and additional FGF pathway genes in ovarian cancers of different histologies (Fig. 2A) and in paired sensitive and resistant ovarian cancer cell lines (Fig. 2B). We have additionally shown that FGF1 expression is significantly inversely correlated with both progression-free (Fig. 2C) and overall survival in ovarian cancer patients [6]. We are therefore currently recruiting patients to longitudinal clinical studies to investigate whether FGF1 or additional related growth factors can predict disease progression and/or the development of treatment-limiting drug resistance.
MicroRNAs (miRNAs) are small non-coding RNAs (19–25 nucleotides) that regulate gene expression by binding to mRNA target sequences and disrupting translation [7]. MiRNAs have great potential as diagnostic and clinical response biomarkers in ovarian and additional cancers as miRNA expression can now routinely be quantitatively assessed in small biopsies and in formalin-fixed material. For example, approximately 30 miRNAs (including miR-21, miR-141, miR-203, miR-205 and miR-214) are differentially expressed in ovarian cancer [8], while miRNAs including miR-200a, miR-200b and miR-429 have also been associated with cancer recurrence and have been shown to predict survival. For example, high expression of miR-200, miR-141, miR-18a and low expression of let-7b, and miR-199a were found to predict poor survival in a cohort of 20 ovarian cancer patients [9]. Meanwhile, recent data from our own laboratory has identified multiple miRNAs including miR-125b and miR-130 associated with the development of platinum resistance. MiRNAs are particularly promising candidate biomarkers due to their stability, and abundant expression in solid cancers, whole blood and routinely collected plasma and serum samples.
Future directions
Due to the challenges of finding a single biomarker that can encompass the complexity and heterogeneity of ovarian cancer it is logical that optimization of a multi-biomarker panel may be the most practical approach, for example combining HE4 and mesothelin with CA125 to augment both sensitivity and specificity. This type of approach has recently been proposed in algorithms such as the Risk of Ovarian Malignancy Algorithm or ROMA which combines CA125 and HE4 levels with a sensitivity of 94% and specificity of 75%. [4]. Combinations of CA125 and mesothelin have also been found to detect more cancers than each biomarker alone. Several current studies have, however, suggested that combination biomarker analysis significantly increases the predictive power of CA125, but also unfortunately appears to decrease specificity. Ongoing studies therefore aim to develop improved biomarker panels suitable both for early detection and treatment guidance of ovarian cancer (Table 2). All of these results still require validation but they are indicative of the possible power of using a multi-biomarker panel in diagnostic tests and for monitoring the clinical responses of ovarian cancer.
Concluding remarks
An ideal biomarker for ovarian cancer will have a high enough sensitivity to correctly diagnose women with the disease and be specific enough to avoid false positive results. With ongoing efforts to identify biomarkers which match this ideal, hundreds of candidates with clinical relevance have been found but still require much validation before having a routine place in the clinic. It is expected that the future of ovarian cancer detection will be based on panels of combination serum-based biomarkers alongside biological imaging techniques to improve diagnosis, treatment and disease management.
References
1. Shapira I, Oswald M, Lovecchio J, Khalili H, Menzin A, Whyte J, Dos Santos L, Liang S, Bhuiya T, Keogh M, Mason C, Sultan K, Budman D, Gregersen PK, Lee AT. Circulating biomarkers for detection of ovarian cancer and predicting cancer outcomes. Br J Cancer 2014; 110: 976–983.
2. Nguyen L, Cardenas-Goicoechea SJ, Gordon P, Curtin C, Momeni M, Chuang L, Fishman D. Biomarkers for early detection of ovarian cancer. Women’s Health 2013; 9: 171–185; quiz 186–187.
3. Sarojini S, Tamir A, Lim H, LI S, Zhang S, Goy A, Pecora A, Suh KS. Early detection biomarkers for ovarian cancer. J Oncol. 2012; 15.
4. Jordan SM, Bristow RE. Ovarian cancer biomarkers as diagnostic triage tests. Current Biomarker Findings 2013; 3: 35–42.
5. Zhang B, Barekati Z, Kohler C, Radpour R, Asadollahi R, Holzgreve W, Zhong XY. Proteomics and biomarkers for ovarian cancer diagnosis. Ann Clin Lab Sci. 2010; 40: 218–225.
6. Smith G, NG MT, Shepherd L, Herrington CS, Gourley C, Ferguson MJ, Wolf CR. Individuality in Fgf1 expression significantly influences platinum resistance and progression-free survival in ovarian cancer. Br J Cancer 2012; 107: 1327–1336.
7. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116: 281–297.
8. Zhang B, Cai FF, Zhong XY. An overview of biomarkers for the ovarian cancer diagnosis. Eur J Obstet Gynecol Reprod Biol. 2011; 158: 119–123.
9. Nam EJ, Yoon H, Kim SW, Kim H, Kim YT, Kim JH, Kim JW, Kim S. MicroRNA expression profiles in serous ovarian carcinoma. Clin Cancer Res. 2008; 14: 2690–2695.
10. Yurkovetsky Z, Skates S, Lomakin A, Nolen B, Pulsipher T, Modugno F, Marks J, Godwin A, Gorelik E, Jacobs I, Menon U, LU K, Badgwell D, Bast RC, JR, Lokshin AE. Development of a multimarker assay for early detection of ovarian cancer. J Clin Oncol. 2010; 28: 2159–2166.
11. SU F, Lang J, Kumar A, NG C, Hsieh B, Suchard MA, Reddy ST, Farias-Eisner R. Validation of candidate serum ovarian cancer biomarkers for early detection. Biomark Insights 2007; 2: 369–375.
12. Zhang Z, YU Y, XU F, Berchuck A, Van Haaften-Day C, Havrilesky LJ, de Bruijn HW, van der Zee AG, Woolas RP, Jacobs IJ, Skates S, Chan DW, Bast RC, Jr. Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. Gynecol Oncol. 2007; 107: 526–531.
13. Gorelik E, Landsittel DP, Marrangoni AM, Modugno F, Velikokhatnaya L, Winans MT, Bigbee WL, Herberman RB, Lokshin AE. Multiplexed immunobead-based cytokine profiling for early detection of ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2005; 14: 981–987.
14. Lokshin AE, Winans M, Landsittel D, Marrangoni AM, Velikokhatnaya L, Modugno F, Nolen BM, Gorelik E. Circulating IL-8 and anti-IL-8 autoantibody in patients with ovarian cancer. Gynecol Oncol. 2006; 102: 244–251.
The authors
Emma L. Joseph1 BSc; Michelle J. Ferguson2 MBChB, MD; and Gillian Smith1* PhD
1Division of Cancer Research, Medical Research Institute, University of Dundee, Dundee UK
2Tayside Cancer Centre, Ninewells Hospital & Medical School, Dundee UK
*Corresponding author
E-mail: g.smith@dundee.ac.uk
Recent findings indicate that aspects of high-density lipoprotein (HDL) not captured by traditionally measured HDL–cholesterol levels (HDL‑C) are likely to be cardioprotective. This review will highlight some of these studies and suggest new directions to identify the specific molecules that are responsible for the cardioprotective nature of HDL.
by Daniel S. Kim, Dr Patrick M. Hutchins and Prof. Gail P. Jarvik
Raising HDL-C does not confer cardioprotection
There is a well-established inverse association between high-density lipoprotein–cholesterol (HDL-C) levels and cardiovascular disease (CVD) in epidemiological and clinical studies [1, 2]. This robust relationship suggested that HDL-C was in the causal pathway of atheroprotection. Indeed a large number of studies have demonstrated that HDL possesses various anti-atherogenic properties, primarily the ability to accept cholesterol from macrophages in a process termed reverse cholesterol transport [3, 4].
In contrast, several high-profile studies have demonstrated that increasing levels of HDL-C does not have a significant cardioprotective effect. In a large and well-conducted clinical trial of the cholesterol ester transport protein (CETP) inhibitor, torcetrapib, there was no reduction in the incidence of CVD-related events despite significantly higher HDL-C levels [5]. A follow-up study using a different CETP inhibitor, dalcetrapib, also showed increased HDL-C levels yet there was no significant difference in CVD event rate between the treatment and placebo groups [6]. In a third randomized clinical trial that used niacin to increase HDL-C levels, there was again no reduction in cardiovascular events [7]. Finally, a large-scale Mendelian randomization study of approximately 20 000 myocardial infarction (MI) cases and 100 000 controls, showed that a genetic polymorphism which associated with approximately 10% higher HDL-C levels was not associated with decreased incidence of MI [8], again suggesting that the relationship between HDL-C and the prevention of cardiac events is not causal.
HDL particle concentration is a superior predictor of CVD
As the elevation of HDL-C was not beneficial in these studies, some have speculated that HDL itself is not cardioprotective. An alternative explanation for these negative data is that the cholesterol content of HDL – a surrogate measure of HDL – does not best reflect the anti-atherogenic properties of HDL. To resolve these issues it is critical to identify new HDL metrics that reliably reflect its cardioprotective functions.
One promising approach for assessing the role of HDL in CVD is to evaluate the individual HDL particles. HDL is a heterogeneous mixture of lipoprotein particles composed of discrete subspecies that have unique structural compositions and biological functions. As different HDL particles carry vastly different amounts of cholesterol – ranging over an order-of-magnitude [9, 10] – measuring the total HDL-C does not provide information regarding the distribution of HDL subpopulations or the number of total HDL particles.
HDL can be fractionated based on a number of physicochemical properties, most commonly size or density. Several techniques, both qualitative and quantitative, have been developed for HDL subspecies analysis. The various HDL subspecies reported by these techniques and their associated nomenclature are briefly summarized in Table 1 [see also ref. 11]. Furthermore, HDL subspecies determined by ultracentrifugation and calibrated ion mobility analysis (both are discussed in detail later) are shown in Figure 1. Many studies have demonstrated the potential clinical utility of HDL subspecies analysis, which can be achieved by techniques such as 2D gradient gel electrophoresis [12] and nuclear magnetic resonance (NMR) [13]. For example, one study (using 2D gradient gel electrophoresis) showed that very-large, cholesterol-rich α-1 HDLs were better predictors than HDL-C levels of reduced coronary heart disease (CHD) in a subset of males from the Framingham Offspring Study [14]. Another high-profile study, using NMR to assess HDL subspecies in over 2200 participants in the EPIC-Norfolk cohort, showed that higher HDL particle (HDL-P) concentrations were a predictor of reduced CHD, independent of classic CHD risk factors [15]. In more recent work from the Multi-Ethnic Study of Atherosclerosis, total HDL-P (measured by NMR) and HDL-C were evaluated at baseline for 5598 participants, who were then followed prospectively for incident CHD (n=227 events) [16]. Although both HDL-P and HDL-C were highly correlated with each other, in multivariate regression models total HDL-P concentration was the superior predictor of reduced incident CHD when compared to HDL-C. This finding indicates that although HDL-C captures a large portion of HDL-P variation, HDL-P is the better predictor of CHD.
These studies support the notion that measuring individual HDL particle subspecies provides clinically useful information beyond traditionally measured HDL-C. However, both α-1 HDLs (which are cholesterol-rich) and HDL-P measured by NMR (which relies on lipid to generate signal) are highly correlated with HDL-C. Therefore, it is possible that these observations reflect a similar inverse association observed between HDL-C and cardiovascular disease. Importantly, two recent studies (discussed below) indicate that low levels of relatively cholesterol-poor, smaller HDLs also associate with cerebrovascular disease, again suggesting that subspecies of HDL not adequately captured by measuring HDL-C may also play important roles in the pathogenesis of atherosclerotic disease.
Shifting focus: HDL-3 and medium-HDL particles
We investigated the association of the subspecies HDL-2 and HDL-3 (Table 1; Fig. 1) with carotid artery disease (CAAD) [17]. Here, HDL was sub-fractionated by ultracentrifugation and the subspecies were quantified by their cholesterol content. In a case-control cohort of 1,725 participants [part of the Carotid Lesion Epidemiology And Risk (CLEAR) cohort], stepwise linear regression was used to determine whether total HDL-C, HDL-2 cholesterol (HDL-2C), HDL-3 cholesterol (HDL-3C), or apolipoprotein A-I (apoA-I) levels were the best predictor of CAAD. In this study, the smaller HDL-3C fraction was found to be the best predictor of reduced CAAD risk. Moreover, adding HDL-3C to the model improved prediction even when HDL-C levels were also considered, demonstrating added utility of the HDL-3C measure versus HDL-C.
In a separate study using calibrated ion mobility analysis, the particle concentrations of three HDL subspecies (Table 1; Fig. 1) were measured in a subset of the same CLEAR cohort [18]. Participants with severe carotid stenosis (n=40; >80% stenosis by ultrasound in either or both internal carotid artery) had significantly lower plasma concentrations of medium-HDL particles compared with control participants (n=40; <15% stenosis by ultrasound in both carotid arteries). In this population HDL-P was a superior predictor of CAAD compared to HDL-C and this relationship was significant after controlling for HDL-C. The case-control difference in total HDL-P was driven by dramatic changes in medium-HDL particles, the next best predictor of CAAD. This medium-HDL particle inverse association also remained significant after controlling for HDL-C. Considering HDL-3 is composed of small- and medium-HDL particles (Fig. 1) and medium-HDL contributes the majority of HDL-3 cholesterol content, these results are in excellent agreement with the previous study of the CLEAR cohort. Both results support the hypothesis that relatively cholesterol-poor, smaller HDL subspecies, which are under-represented by total HDL-C, are potentially important protective factors for CVD.
Summary and future directions
Considering that increased levels of cholesterol-poor HDL subspecies – reflected by measures of HDL-3, medium size particles, and increased HDL-P – can represent superior predictors of CVD phenotypes, it is possible that pharmacologic attempts to raise HDL-C fail to affect CVD event rates because specifically elevating the cholesterol content of HDL is insufficient. The mechanism of HDL-C elevation should be considered. The agents tested thus far may have increased HDL-C by forming large, cholesterol-rich HDL particles at the expense of medium- and small-HDL particles; having an overall null effect on total particle concentration. Indeed, there is evidence from 2D-gel electrophoresis that very high HDL-C levels observed in CETP deficiency result from a shift from small- and medium-HDLs to large-HDL particles [19]. Thus, HDL directed therapies – especially CETP inhibitors – might increase HDL-C without increasing the number of total HDL-P and possibly reducing the number of potentially beneficial medium-HDL particles. Considering that medium- and total HDL particle concentrations may represent superior predictors of cardioprotection, this hypothesis could explain the failures of the CETP inhibitors and niacin to prevent CVD. We speculate that HDL directed therapies might be more effective in reducing CVD-related events if the number of circulating HDL particles was increased by therapy, especially medium-HDLs.
In light of recent research showing that certain subspecies of HDL (such as medium-HDL and HDL-3) may specifically contribute to cardioprotection, it is our opinion that the focus of research and potential therapies should shift to these promising targets. Of particular interest is the protein cargo of these HDL subspecies, which may reveal important mechanisms related to their cardioprotective properties. For instance, HDL-3 is closely associated with PON1 enzyme activity [20], which is associated with cardioprotection [21, 22]. Notably, the cardioprotective association of HDL-3 was in part independent of both PON1 activity and HDL-C, indicating that there were unmeasured predictive elements of the HDL-3 proteome; these may be apolipoproteins, or ancillary proteins that are specifically associated with HDL-3 [17].
In summary, it is our opinion that the recent failure of increased HDL-C to be cardioprotective likely reflects the fact that increasing HDL-C alone does not adequately increase the concentration or activity of cardioprotective HDL subspecies. It would be an error to say that studies of HDL-C demonstrate that HDL is not cardioprotective. Increased total HDL particle concentration, or perhaps a specific increase in medium-HDL particles, may confer greater protection against CAAD and CHD than pharmaceutically generating a preponderance of large, cholesterol-rich HDL particles. Future research should focus on narrowing down focus through computational, structural and functional studies to identify the specific molecule or molecules that are responsible for the expected cardioprotective effect of HDL.
References
1. Castelli WP. Cardiovascular disease and multifactorial risk: challenge of the 1980s. Am Heart J. 1983; 106: 1191–1200.
2. Gordon DJ, Rifkind BM. High-density lipoprotein–the clinical implications of recent studies. N Engl J Med. 1989; 321: 1311–1316.
3. Rye KA, Bursill CA, Lambert G, Tabet F, Barter PJ. The metabolism and anti-atherogenic properties of HDL. J Lipid Res. 2008; 50: S195–S200.
4. Oram JF, Heinecke JW. ATP-binding cassette transporter A1: a cell cholesterol exporter that protects against cardiovascular disease. Physiol Rev. 2005; 85: 1343–1372.
5. Barter PJ, Barter PJ, Caulfield M, Caulfield M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med. 2007; 357: 2109–2122.
6. Schwartz GG, Olsson AG, Abt M, Ballantyne CM, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012; 367: 2089–2099.
7. AIM-HIGH Investigators, Boden WE, Probstfield JL, Anderson T, et al. Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med. 2011; 365: 2255–2267.
8. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012; 380: 572–580.
9. Shen BW, Scanu AM, Kézdy FJ. Structure of human serum lipoproteins inferred from compositional analysis. Proc Natl Acad Sci U S A. 1977; 74: 837–841.
10. Huang R, Silva RAGD, Jerome WG, Kontush A, et al. Apolipoprotein A-I structural organization in high-density lipoproteins isolated from human plasma. Nat Struct Mol Biol. 2011; 18: 416–422.
11. Rosenson RS, Brewer HB, Chapman MJ, Fazio S, et al. HDL measures, particle heterogeneity, proposed nomenclature, and relation to atherosclerotic cardiovascular events. Clin Chem. 2011; 57: 392–410.
12. Asztalos BF, Sloop CH, Wong L, Roheim PS. Two-dimensional electrophoresis of plasma lipoproteins: recognition of new apo A-I-containing subpopulations. Biochim Biophys Acta 1993; 1169: 291–300.
13. Otvos JD. Measurement of lipoprotein subclass profiles by nuclear magnetic resonance spectroscopy. Clin lab. 2002; 48: 171–180.
14. Asztalos BF, Cupples LA, Demissie S, Horvath KV, et al. High-density lipoprotein subpopulation profile and coronary heart disease prevalence in male participants of the Framingham Offspring Study. Arterioscler Thromb Vasc Biol. 2004; 24: 2181–2187.
15. Harchaoui El K, Arsenault BJ, Franssen R, Després J-P, et al. High-density lipoprotein particle size and concentration and coronary risk. Ann Intern Med. 2009; 150: 84–93.
16. Mackey RH, Greenland P, Goff DC, Lloyd-Jones D, et al. High-density lipoprotein cholesterol and particle concentrations, carotid atherosclerosis, and coronary events: MESA (multi-ethnic study of atherosclerosis). J Am Coll Cardiol. 2012; 60: 508–516.
17. Kim DS, Burt AA, Rosenthal EA, Ranchalis JE, et al. HDL-3 is a superior predictor of carotid artery disease in a case-control cohort of 1725 participants. J Am Heart Assoc. 2014; 3: e000902.
18. Hutchins PM, Ronsein GE, Monette JS, Pamir N, et al. Quantification of HDL particle concentration by calibrated ion mobility analysis. Clin Chem. 2014; 60: 1393–1401.
19. Asztalos BF. Apolipoprotein composition of HDL in cholesteryl ester transfer protein deficiency. J Lipid Res. 2003; 45: 448–455.
20. Kontush A, Chantepie S, Chapman MJ. Small, dense HDL particles exert potent protection of atherogenic LDL against oxidative stress. Arterioscler Thromb Vasc Biol. 2003; 23: 1881–1888.
21. Jarvik GP, Rozek LS, Brophy VH, Hatsukami TS, et al. Paraoxonase (PON1) Phenotype Is a Better Predictor of Vascular Disease Than Is PON1192 or PON155 Genotype. Arterioscler Thromb Vasc Biol. 2000; 20: 2441–2447.
22. Kim DS, Marsillach J, Furlong CE, Jarvik GP. Pharmacogenetics of paraoxonase activity: elucidating the role of high-density lipoprotein in disease. Pharmacogenomics 2013; 14: 1495–1515.
The authors
Daniel Seung Kim1–3† BS; Patrick M. Hutchins4† PhD; Gail P. Jarvik1,2 MD, PhD
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
2Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
3Department of Biostatistics, University of Washington, Seattle, WA, USA
4Division of Metabolism, Endocrinology, and Nutrition, Department of Medicine, University of Washington, Seattle, WA, USA
†Authors contributed equally to this work
*Corresponding author
E-mail: pair@u.washington.edu
Acknowledgement
DSK is supported in part by 1F31MH101905-01 and a Markey Foundation Award. PMH is supported by a Cardiovascular Fellowship Training Grant (NIH T32HL007828). Work on the CLEAR study referenced within was supported by National Institutes of Health RO1 HL67406 and a State of Washington Life Sciences Discovery Award (265508) to the Northwest Institute of Genetic Medicine.
November 2024
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