Beukenlaan 137
5616 VD Eindhoven
The Netherlands
+31 85064 55 82
info@clinlabint.com
PanGlobal Media is not responsible for any error or omission that might occur in the electronic display of product or company data.
Featured Articles
Rapid and reliable detection of medulloblastoma-associated DNA methylation patterns: MS-MIMIC
Disease-associated variations in DNA methylation profiles hold significant potential for diagnostic and research applications. Unfortunately, scant and degraded samples often limit the analyses which can be performed. To overcome these issues, we developed Mass Spectrometry Minimal Methylation Classifier (MS-MIMIC) to identify then reliably analyse disease-specific DNA methylation profiles. The technique has now been validated in a cohort of pediatric medulloblastoma cases.
by Dr Ben Chaffey, Dr Debbie Hicks, Dr Edward Schwalbe and Prof. Steve Clifford
Background
Altered DNA methylation patterns have emerged as valuable biomarkers of disease pathogenesis, showing clear potential in diagnostics, sub-classification and prediction of therapeutic response/ disease course [1–7]. However, clinical assessment of these altered patterns can be problematic, with sample materials often being degraded/scant, such as formalin-fixed paraffin-embedded (FFPE) tissue and core biopsies, and certain platforms, such as DNA methylation microarrays, having a requirement for batched assessments of relatively large numbers of samples. This compromises generation of data from real-world samples within clinically meaningful timeframes, hampering translation of research findings into routine practice.
In this project, we focused on developing a new DNA methylation state assay to provide molecular subgrouping of cases of medulloblastoma (Fig. 1), the most frequently occurring malignant brain tumour in children. This disease has an approximate incidence of 1.5 cases per million, rising to 6 per million in children aged 1–9 years. It also occurs in adults, although in this group it is around ten times less common [8].
Although rates of survival to 5 years and beyond following diagnosis are around 65–70%, medulloblastoma still causes around 10% of all childhood cancer deaths. Initial treatment generally consists of complete or near complete surgical resection followed by adjuvant treatment with both post-operative radiotherapy and chemotherapy. Despite the fact that survival rates have improved over past decades, the delivery of individualized therapies based on patient-specific disease-risk profiles remains a major goal; intensified treatment for poor-risk disease, while reducing therapy for favourable-risk cases, with the overall aim of maximizing survival while minimizing late effects [9].
Medulloblastomas can be placed into one of four distinct subgroups, which are defined by specific methylomic, transcriptomic and genomic features. These are WNT, SHH, Group 3 and Group 4 [10]. Each group displays characteristic clinical and pathological features, drug targets and outcomes, and contributes significantly to the 2016 World Health Organization (WHO) classification of brain tumours [11]. Molecular subgrouping is, therefore, an important step in determining the most appropriate course of treatment and follow-up for individual patients [12].
Mass spectrometry minimal methylation classifier (MS-MIMIC) assay
The assay we have developed and validated, MS-MIMIC, is a novel polymerase chain reaction (PCR)-based assay for the multiplexed assessment of multiple signature CpG loci. We first identified a DNA methylation signature of 17 CpG loci using genome-scale Illumina 450k DNA methylation microarray data from 220 medulloblastoma cases. The 50 most discriminatory CpG loci for each molecular subgroup (200 loci in total) were considered as candidates for inclusion in the signature set. These were triaged using a 10-fold cross validated classification fusion algorithm, followed by a reiterative primer design process where amenability to primer design and multiplex bisulfite PCR was assessed in silico before finally undergoing in vitro PCR validation.
Candidate signature CpG loci were then analysed by a specific custom iPLEX assay [13] (Agena Bioscience). In this method (displayed schematically in Fig. 2), methylation-dependent SNPs representative of CpG methylation status are induced by initial treatment of DNA with sodium bisulfite [14] followed by multiplexed target-region amplification PCR, then single base extension and termination of target-specific probe oligonucleotides. The products of this reaction are analysed using MALDI-ToF (matrix-assisted laser desorption and ionisation – time of flight) mass spectrometry (MassARRAY System, Agena Bioscience). Each potential CpG locus variant yields a product with a unique and characteristic mass, enabling their rapid and unambiguous identification. MALDI-ToF analysis of single base variants is widely used to provide clinical DNA diagnostics in related genotyping applications [15], and is the key technical innovation which enables the robust assessment of medulloblastoma molecular subgroup, especially for samples which are refractory to analysis using conventional DNA methylation-array based methods.
Using these techniques, we generated an optimal, multiply-redundant 17-CpG locus signature and a robust assay for its detection.
A Support Vector Machine (SVM) classifier for the signature was then developed, using the existing 450k DNA methylation array data as a training set. SVM is a supervised machine learning technique commonly used in multiple areas of data analysis, including analysis of microarray data [16], making it well-suited to this application. Crucially, it returns a probability of group membership, enabling the assessment of confidence of subgroup assignment.
Next, we assessed MS-MIMIC performance against Illumina 450k methylation microarrays using an independent validation cohort of 106 medulloblastoma DNA samples which contained examples of all four medulloblastoma subgroups. These samples were also derived from tissue which reflected different clinical fixation methods commonly in use; fresh-frozen biopsies (n=40), FFPE tumour section (n=39), or FFPE-derived nuclear preparations [17] (n=27) produced by cytospin, a pre-analytical method that uses centrifugation to create a monolayer of cells for analysis on a slide from a low-concentration cellular suspension sample [18]. In this validation cohort, MS-MIMIC faithfully recapitulated DNA methylation array molecular subgroup assignments.
Quality control measures for CpG locus-specific assay failure were established; up to six failed CpG loci per sample were tolerated within the multiply-redundant signature/classifier, without impacting performance. Forty-three out of 106 validation cohort samples were affected by at least one locus failure, reflecting the damaged nature of DNA generally obtained from some of these samples. Five out of 106 samples had more than seven failed CpGs and were deemed not classifiable (NC). Molecular subgroup classifications were then compared, with MS-MIMIC classifications showed complete concordance with the reference subgroup, as determined by DNA methylation array [10]. Furthermore, CpG-level methylation estimates (β-values) were equivalent between methods (R2 = 0.79). As anticipated, fresh-frozen derivatives performed best (n=39/40; 98% successfully subgrouped), with 91% success (n=56/61) using FFPE-derived DNA from tumour sections and cytospin preparations (Fig. 3a–c).
Application of MS-MIMIC to the HIT-SIOP-PNET4 clinical trials cohort
Following successful assay development and validation, we next wished to test MS-MIMIC methylation signature detection in limited, poor quality, archival, clinical biopsies. Analysis of remnant material from the HIT-SIOP-PNET4 cohort [17] offered the first opportunity to determine the potential utility of molecular subgroup status to predict disease outcome in a clinical trial of risk-factor negative, ‘standard risk’ (SR) medulloblastoma. Only FFPE sections (n=42/153 available tumour samples) and cytospin nuclear preparations (approximately 30 000 nuclei isolated and centrifuged onto microscope slides; n=111/153) remained from this study archive and all DNA preparations fell below quality and quantity thresholds (>200 ng double-stranded DNA (dsDNA)) required for methylation profiling using conventional research methods (Illumina 450k and MethylationEpic arrays [16]). Using MS-MIMIC, 70% (107/153) of samples were successfully subgrouped, and subgroup assignments and β-value estimations were consistent across duplicate determinations. Assay performance was equivalent across the input DNA range (<2 ng (limit of detection) to 100 ng dsDNA (41.4 ng median DNA input).
Reasons for assay failure included unsuccessful bisulfite conversion/PCR (6%; 9/153), and inability to classify due to assay QC failure (24%; 37/153). These findings from HIT-SIOP-PNET4 reveal important subgroup-dependent molecular pathology in SR medulloblastoma. Group 4 was most common (n=62; 58%), with approximately equivalent numbers of WNT (18/170; 16%), SHH (17/107; 16%) and Group 3 (10/107; 9%) tumours observed. The majority (11/13) of events (defined as disease recurrence or progression following treatment) affected Group 4 patients [82% 5-year progression-free survival (PFS)], with >95% PFS in non-Group 4 patients. Subgroup assignment will thus be essential to inform future clinical and research studies in SR medulloblastoma.
Discussion
Detection of disease-specific variations in DNA methylation patterns has great potential for both supporting biomedical research and improving the quality of care that is delivered to patients. MS MIMIC has so far only been applied to medulloblastoma but this approach has clear potential for use in other cancers [7] and in other diverse settings, for example smoking [1], obesity [2], human fetal alcohol spectrum disorder [3] and aging [4].
Key resources which must be available for development of an MS-MIMIC assay for a given condition are a suitable collection of data concerning disease-state specific methylation patterns obtained using an array system such as those mentioned above, samples with which to perform assay validation, bioinformatics knowledge and support to create, optimize and operate the disease-specific SVM classifier system, plus access to a MassARRAY System for analysis. MS-MIMIC is discussed in greater detail in Schwalbe et al., 2017 [19].
References
1. Besingi W, Johansson A. Smoke-related DNA methylation changes in the etiology of human disease. Hum Mol Genet 2014; 23: 2290–2297.
2. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, Tsai PC, Ried JS, Zhang W et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 2017; 541: 81–86.
3. Portales-Casamar E, Lussier AA, Jones MJ, MacIsaac JL, Edgar RD, Mah SM, Barhdadi A, Provost S, Lemieux-Perreault LP et al. DNA methylation signature of human fetal alcohol spectrum disorder. Epigenetics Chromatin 2016; 9: 1–20.
4. Ong ML, Holbrook JD. Novel region discovery method for Infinium 450 K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell 2014; 13: 142–155.
5. Mehta D, Klengel T, Conneely KN, Smith AK, Altmann A, Pace TW, Rex-Haffner M, Loeschner A, Gonik M et al. Childhood maltreatment is associated with distinct genomic and epigenetic profiles in posttraumatic stress disorder. Proc Natl Acad Sci USA 2013; 110: 8302–8307.
6. Bacalini MG, Gentilini D, Boattini A, Giampieri E, Pirazzini C, Giuliani C, Fontanesi E, Scurti M, Remondini D et al. Identification of a DNA methylation signature in blood cells from persons with Down Syndrome. Aging 2015; 7: 82–96.
7. Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, Tönjes M, Sill M et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 2012; 22: 425–437.
8. Smoll NR, Drummond KJ. The incidence of medulloblastomas and primitive neurectodermal tumours in adults and children. J Clin Neurosci 2012; 19: 1541–1544.
9. Pizer BL, Clifford SC. The potential impact of tumour biology on improved clinical practice for medulloblastoma: progress towards biologically driven clinical trials. British Journal Of Neurosurgery 2009; 23: 364–375.
10. Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ, Clifford SC, Eberhart CG, Parsons DW, Rutkowski S et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 2012; 123, 465–472.
11. Louis DN, Cavenee WK, Ohgaki H, Wiestler OD. WHO classification of tumours of the central nervous system, 4th edn. pp.184–200. IARC Press, 2016.
12. Schwalbe EC, Williamson D, Lindsey JC, Hamilton D, Ryan SL, Megahed H, Garami M, Hauser P, Dembowska-Baginska B et al. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol 2013; 125: 359–371.
13. Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Current Protocols in Human Genetics, Chapter 2: Unit 2.12. Wiley & Sons 2009.
14. Wang RY, Gehrke CW, Ehrlich M. Comparison of bisulfite modification of 5-methyldeoxycytidine and deoxycytidine residues. Nucleic Acids Res 1980; 8: 4777–4790.
15. Griffin TJ, Smith LM. Single-nucleotide polymorphism analysis by MALDI-ToF mass spectrometry. Trends Biotechnol 2000; 18: 77–84.
16. Hovestadt V, Remke M, Kool M, Pietsch T, Northcott PA, Fischer R, Cavalli FM, Ramaswamy V, Zapatka M et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol 2013; 125: 913–916.
17. Clifford SC, Lannering B, Schwalbe EC, Hicks D, O’Toole K, Nicholson SL, Goschzik T, Zur Mühlen A, Figarella-Branger D et al. Biomarker-driven stratification of disease-risk in non-metastatic medulloblastoma: Results from the multicentre HIT-SIOP-PNET4 clinical trial. Oncotarget 2015; 6: 38827–38839.
18. Koh CM. Preparation of cells for microscopy using cytospin. Meth Enzymol 2013; 533: 235–240.
19. Schwalbe EC, Hicks D, Rafiee G, Bashton M, Gohlke H, Enshaei A, Potluri S, Matthiesen J, Mather M et al. Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. Sci Rep 2017; 7: 13421.
The authors
Ben Chaffey1 PhD, Debbie Hicks2 PhD, Edward Schwalbe3 PhD, Steve Clifford2* PhD
1NewGene Ltd, International Centre for Life, Newcastle-upon-Tyne, UK
2Wolfson Childhood Cancer Research Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK
3Northumbria University, Newcastle-upon-Tyne, UK
*Corresponding author
E-mail: steve.clifford@ncl.ac.uk
Genomic tracking of MRSA outbreaks could help infection control
Hospital-acquired infections (HAIs) today rank among the major causes of death and morbidity in hospitalized patients and are estimated to be responsible for 175,000 deaths per year in industrialized countries. HAIs have been growing exponentially worldwide since the 1980s primarily because of the indiscriminate use of antibiotics which have triggered the growth of multidrug resistant bacterial strains – also known as superbugs – and the transmission of such strains between patients, as well as between patients and hospital staff and vice versa. Methicillin-resistant Staphylococcus aureus (MRSA) is a superbug that is resistant to several widely used antibiotics. In the general community, MRSA mostly causes skin infection, is spread by skin-to-skin contact and, if left untreated, can also get deeper into the body, causing potentially life-threatening infections. It is generally estimated that about 3 percent of the population chronically carries MRSA. However, in a healthcare setting such as a hospital or nursing home, MRSA infection is more frequent and often more severe, leading to pneumonia, surgical site infections, bloodstream infections and possibly sepsis. The risk factors are indeed much higher in hospitals because of the increased vulnerability of some patients (the elderly and those with weakened immune systems) and because of the multiple potential pathways for MRSA entry into the body provided by wounds (including surgical wounds), burns as well as feeding tubes, intravenous lines or urinary catheters. MRSA is also prevalent in nursing homes where healthy carriers have the opportunity to spread it among the resident population and staff.
A very recent study, published in the October 25 edition of Science Translational Medicine, used genomic sequencing technology for the genomic surveillance of MRSA in the East of England. A team at the Wellcome Trust Sanger Institute sequenced the genetic code of every single MRSA-positive sample processed over a 12-month period by a routine clinical microbiology lab receiving samples from three hospitals and 75 general practitioner practices. Samples from 1465 people were analysed, revealing a total of 173 transmission clusters involving 598 people and ranging from outbreaks affecting two patients up to 44. These findings shed some new light on MRSA transmission within and between hospitals and the community and could pave the way for more targeted, efficient and effective infection control practices. While genomic surveillance of MRSA cannot by itself prevent an outbreak from occurring, it can certainly help to reduce the numbers of infected people. The cost-effectiveness of implementing this strategy needs to be carefully evaluated. Although the whole genome of a bacterium can now be sequenced for around 140€, this might still prove too much for many healthcare systems.
LC-MS/MS measurement of serum steroids in the clinical laboratory
In recent decades liquid chromatography–tandem mass spectrometry (LC-MS/MS) has become more widespread in the clinical laboratory, bridging the analytical gap between high-throughput (but interference prone) immunoassays and the highly specific (but labour intensive) technique of gas chromatography–mass spectrometry (GC-MS). This article discusses serum steroid measurement by LC-MS/MS and describes a multiplexed LC-MS/MS steroid panel recently launched at Imperial College Healthcare NHS Trust.
by Dr Emma L. Williams
Introduction
Historically steroid hormones have been measured, primarily in urine, by GC-MS and in serum and plasma by radio-immunoassay. Both techniques require sample extraction prior to analysis and for the former there is a need for derivatization to form volatile derivatives. Thus the assays are laborious and time consuming and have been the preserve of research and specialist laboratories. More recently automated immunoassays have been used in routine clinical laboratories, but these are notorious for being highly prone to interference as a result of their inherent specificity problems [1]. In recent decades LC-MS/MS has come to the fore, offering a promising alternative to immunoassays for high-throughput, specific measurement of serum steroids and it is now the method of choice in many clinical laboratories. LC-MS/MS measurement of serum steroids is informative in the clinical investigation of conditions such as hirsutism, polycystic ovarian syndrome (PCOS) and infertility. In addition LC-MS/MS steroid measurement forms part of a diagnostic triad, along with urine steroid profiling by GC-MS and whole gene sequencing of genomic DNA, for inherited steroidogenic defects including the congenital adrenal hyperplasias (CAH) and disorders of sexual differentiation.
LC-MS/MS measurement
Significant advances in LC-MS/MS technology have enabled the development of high-throughput, sensitive and precise assays for steroid measurement. Figure 1 depicts the biosynthetic pathways of steroidogenesis. LC-MS/MS assays have now been published for all of the steroids in this pathway, using a variety of approaches for sample preparation prior to analysis. Protein precipitation, liquid–liquid extraction, solid phase extraction and supported liquid extraction have all been used for the preparation step. In my laboratory, semi-automated off-line solid phase extraction has been implemented in order to achieve higher throughput. This extraction approach is used to prepare samples prior to ultra-performance (UP)LC-MS/MS analysis using electrospray ionization with detection by multiple reaction monitoring (MRM). The majority of steroids are measured in positive ionization mode, although we use negative ionization mode for aldosterone and dehydroepiandrosterone sulphate (DHEAS).
For accurate LC-MS/MS quantitation, stable isotope internal standards (IS) are required. Addition of IS to all samples, calibrators and quality controls (QCs) is carried out prior to extraction and LC-MS/MS analysis. The ratios of analyte to IS signals are determined to correct for effects of the matrix upon signal intensity, which may be due to ion suppression or enhancement. Typically in LC-MS/MS assays the IS will have two or more hydrogens replaced by deuterium atoms. The IS has a different mass and ion transition to the analyte, while retaining its chemical and physical properties and thus behaves the same way as the analyte throughout the analytical procedure. Carbon-13 labelled IS are increasingly being used as they have become more available. These co-elute more completely with the non-labelled analyte and are, therefore, more effective at correcting for matrix effects compared to deuterium labelling, which alters polarity and increases the possibility of non-co-elution.
An important factor to consider in steroid LC-MS/MS assays is that of specificity, given the similarities in structures of the various steroid intermediates in the steroidogenic pathway.
There are several examples of steroids that have the same molecular weight and are, therefore, isobaric. It is vital that these isobaric steroids are chromatographically resolved as they will undergo the same ion transitions in the mass spectrometer. If not resolved, they would be measured as if they were the same steroid and, therefore, be a cause of positive interference. For example 11-deoxycortisol and 21-deoxycortisol have the same molecular weight (Fig. 2) and undergo the same ion transitions, but can be chromatographically resolved using the selectivity of the mobile phase. It can be seen in Figure 3 that these steroids are successfully resolved in our laboratory method, which uses reverse phase T3 chromatography.
LC-MS/MS steroid assays
In the clinical laboratory, testosterone is the serum steroid most frequently measured by LC-MS/MS analysis. In the external quality assessment scheme offered by the United Kingdom National External Quality Assessment Service (UK NEQAS), 43 (21%) participating labs use LC-MS/MS, with the remainder relying upon automated immunoassays. In my laboratory, both measurement techniques are used, whereby all female samples with elevated immunoassay testosterone results >2.0 nmol/L are reflexed for LC-MS/MS confirmation. In a recent audit of over 5000 female samples in which testosterone was measured we found that of over 800 elevated samples reflexed for confirmation, 23% of these are subsequently found to have normal LC-MS/MS results within the reference range. It is, therefore, essential that elevated female immunoassay results are confirmed by LC-MS/MS to avoid falsely elevated results being reported. Norethisterone, a synthetic form of progesterone used in hormonal contraceptives, is a commonly encountered cause of positive interference in immunoassays for testosterone in female samples [2].
Advantages of multiplexed assays
Testosterone is measured in the investigation of females presenting with clinical signs of hyperandrogenism, e.g. acne and hirsutism and in the investigation of infertility and PCOS. Following the introduction of LC-MS/MS assays into the clinical laboratory for the combined measurement of testosterone and androstenedione it became clear that androstenedione is the cause of hyperandrogenism in a subgroup of patients with PCOS [3]. These cases previously may have been undiagnosed when the testosterone measured in isolation was found to be normal. This observation highlights the benefits of being able to measure two or more steroids simultaneously, which is not possible with radio-immunoassays or in routine automated immunoassays.
17-Hydroxyprogesterone (17-OHP) measurement is used to screen for 21-hydroxylase deficiency; the most common cause of CAH, accounting for ~85% of cases. 17-OHP sits at a branch point for either cortisol or androgen synthesis (Fig. 1) and accumulates when 21-hydroxylase is deficient. However, it can also be raised in normal newborns, particularly in premature neonates, and is influenced by birth weight and stress. In 21-hydroxylase deficiency, 21-deoxycortisol is formed as a side product from the accumulated 17-OHP in a reaction catalysed by 11-beta hydroxylase. The LC-MS/MS measurement of 21-deoxycortisol for the diagnosis of CAH was first described by Cristoni et al. [4] and it allows accurate diagnosis of 21-hydroxylase deficiency in newborns independent of prematurity, birth weight and stress [5]. Shackleton has proposed that a second tier panel comprising 17-OHP, cortisol, 21-deoxycortisol and androstenedione is used in newborn screening for 21-hydroxylase deficiency with a third tier of urinary GC-MS analysis to clinch the final diagnosis [6]. The addition of 11-deoxycortisol to this panel permits the diagnosis of 11-beta-hydroxylase deficiency, the second most common form of CAH. Such a panel has been applied to second tier testing for CAH [7].
In my laboratory a semi-automated solid phase extraction (SPE) LC-MS/MS method for the simultaneous measurement of androstenedione, testosterone and 17-OHP has been in use since April 2016. The SPE uses Waters Oasis PRiME HLB, 96 well, μ-elution plates and is performed using a Tecan Freedom Evo automated Liquid Handler. One hundred microlitres of sample is mixed with IS and proteins are precipitated with methanol and water. Supernatants are applied to the wells of the SPE plate and drawn through under vacuum. Following washing with 0.1% formic acid in 35% methanol, steroids are eluted with methanol and water enabling direct LC-MS/MS analysis of the eluates.
Using a Waters Acquity UPLC system, samples are injected onto a Waters Acquity UPLC HSS T3 column (2.1 × 50 mm) and separated by water/methanol/ammonium acetate/formic acid gradient elution. The analysis is performed using a Waters Acquity-TQD mass spectrometer in electrospray positive ionization mode. The analytes and their co-eluting isotopic ISs are detected using MRM. Quantifier transitions (m/z) monitored are 287>97 for androstenedione, 289>97 for testosterone and 331>97 for 17-OHP.
The method underwent full validation prior to implementation according to Clinical and Laboratory Standards Institute (CLSI) guidelines and as recommended by Honour [8] and demonstrated excellent linearity over the analytical range, with all r2 values ≥0.99. Overall process efficiency was 100–108.3%, demonstrating excellent recovery and minimal ion suppression/enhancement. Intra-assay precision was 2.6–8.1% for all analytes across the measurement range, and inter-assay precision varied from 4.9 to 10.8%. Analysis of UK NEQAS samples revealed minimal negative bias and the high specificity of the assay was confirmed by spiking and interference studies. The newly developed assay compared favourably with the stand-alone LC-MS/MS methods in use previously in our laboratory, with no requirement to re-derive reference intervals. This supra-regional assay service (SAS) accredited steroid panel assay has been in routine use in our LC-MS/MS laboratory since April 2016, streamlining the analytical service. The assay is carried out two or three times a week, with each full plate accommodating around 80 patient samples, plus standards and controls, with automated sample extraction completed in ~ 90 minutes and the LC-MS/MS sample to sample injection time is 5 minutes.
We have recently evaluated a seven steroid LC-MS/MS assay with the addition of cortisol, DHEAS, 11-deoxycortisol and 21-deoxycortisol into the panel. Figure 3 shows the total ion chromatogram of the steroids quantified by this assay. Using a Waters Acquity-TQD mass spectrometer and a slightly modified experimental set-up, the lower limits of quantification obtained were 16.5 nmol/L for cortisol, 2nmol/L for DHEAS, 7nmol/L for 11-deoxycortisol and 2nmol/L for 21-deoxycortisol.
In conclusion, LC-MS/MS steroid panels are a valuable addition to the diagnostic work up of patients being investigated for hyperandrogenism and in the investigation of steroidogenic defects. The increased availability of semi-automated, high-throughput LC-MS/MS assays for multiplexed steroid measurement has opened the door for their future application in targeted metabolomic research. Finally, in the clinical laboratory setting the future continues to look bright for the role of accurate and robust measurement by LC-MS/MS in place of immunoassays as the method of choice for routine serum steroid measurement.
References
1. Jones AM, Honour JW. Unusual results from immunoassays and the role of the clinical endocrinologist. Clin Endocrinol Oxf 2006; 64: 234–244.
2. Jeffery J, MacKenzie F, Beckett G, Perry L, Ayling R. Norethisterone interference in testosterone assays. Ann Clin Biochem 2014; 51: 284–288.
3. Livadas S, Pappas C, Karachalios A, Marinakis E, Tolia N, Drakou M, Kaldrymides P, Panidis D, Diamanti-Kandarakis E. Prevalence and impact of hyperandrogenemia in 1218 women with polycystic ovarian syndrome. Endocrine 2014; 47: 631–638.
4. Cristoni S, Cuccato D, Sciannamblo M, Bernardi LR, Biunno I, Gerthoux P, Russo G, Weber G, Mora S. Analysis of 21-deoxycortisol, a marker of congenital adrenal hyperplasia, in blood by atmospheric pressure chemical ionization and electrospray ionization using multiple reaction monitoring. Rapid Commun Mass Spectrom 2004; 18: 77–82.
5. Janzen N, Peter M, Sander S, Steuerwald U, Terhardt M, Holtkamp U, Sander J. Newborn screening for congenital adrenal hyperplasia: additional steroid profile using liquid chromatography-tandem mass spectrometry. J Clin Endocrinol Metab 2007; 92: 2581–2589.
6. Shackleton C. Clinical steroid mass spectrometry: a 45-year history culminating in HPLC-MS/MS becoming an essential tool for patient diagnosis. J Steroid Biochem Mol Biol 2010; 121: 481–490.
7. Rossi C, Calton L, Hammond G, Brown HA, Wallace AM, Sacchetta P, Morris M. Serum steroid profiling for congenital adrenal hyperplasia using liquid chromatography-tandem mass spectrometry. Clin Chim Acta 2010; 411: 222–228.
8. Honour JW. Development and validation of a quantitative assay based on tandem mass spectrometry. Ann Clin Biochem 2011; 48: 97–111.
The author
Emma L. Williams PhD, FRCPath
North West London Pathology, Imperial College Healthcare NHS Trust, London
W6 8RF, UK
E-mail: emma.walker15@nhs.net
Cervical cancer screening tests: HPV testing will replace Pap smear
In the 1980s, cervical screening tests were introduced for the detection of abnormal cervical cells (the cytology-based Pap smear test). Since then there has been a reduction in the number of cervical cancer cases by about 7% each year. Under current guidelines in the UK, women are offered 12 tests per lifetime, with frequency based on age: every 3 years for 25–49-year-olds, every 5 years for 50–64-year-olds, and only in certain circumstances for women over 65.
We have been aware for some time that certain high-risk types of human papillomavirus (HPV) are the causative agents of virtually all cases of cervical cancer and a new cervical test procedure is set to be introduced in England by 2019 that will first test samples for HPV and then only check for abnormal cells if the virus is found. Primary HPV testing has a higher sensitivity, lower false-negative rate and is more cost-effective than cytology, thus allowing further resources and cytology-based tests to be reserved for the closer follow-up of women who test positive for high-risk HPV types.
This change is being introduced at around the same time that the first women to be vaccinated against HPV are about to enter the screening system. In the UK, vaccination of girls against HPV began in 2008 using Gardasil, which protects against HPV 16 and 18 as well as 6 and 11 (responsible for approximately 90% of cases of benign genital warts) and dramatically reduces the risk of cervical cancer. Gardasil-9 offers protection against nine HPV types, adding 31, 33, 45, 52 and 58 to the four mentioned above, but is currently only available privately in the UK. Recent research by Landy et al. in the International Journal of Cancer (2017; doi: 10.1002/ijc.31094) suggests that with the use of primary HPV testing, the screening programme should be personalized based on vaccination status, with perhaps as few as two lifetime tests needed for women who have received the nonavalent vaccine, three for the quadrivalent vaccine and seven for unvaccinated women.
However, the researchers also note that with many fewer tests, it is crucial that participation in screening is high; however, recent figures revealed that less than three-quarters of women take up screening invitations. Perhaps this would improve if the method of sample collection was changed from a cervical smear to a urine-based HPV test.
Urinary galactitol quantitation by gas chromatography–mass spectrometry for the diagnosis of galactosemia
Galactosemia is an inborn error of metabolism caused by the deficiency of any of the three principal enzymes (GALT, GALK and GALE) involved in the Leloir pathway. The application of urinary galactitol as a diagnostic and monitoring marker for galactosemia has been extensively researched but the practice varies in different centres. The Willink Biochemical Genetic Laboratory has recently developed and evaluated a method to quantitate urinary galactitol by gas chromatography–mass spectrometry and revisited its use as a first-line diagnostic test for galactosemia. The analytical performance characteristics of the method, established age-related reference ranges, and the relationship between urinary galactitol excretion and hepatic dysfunctions will be discussed.
by Yuh Luan Choo, Teresa Hoi-Yee Wu, Jackie Till and Dr Mick Henderson
Galactosemia: an overview
Galactosemia is a group of three inborn errors of galactose metabolism each with an autosomal recessive inheritance pattern. The deficiency or absence of galactose-1-phosphate uridyltransferase (GALT), galactokinase (GALK) or galactose-4-epimerase (GALE) enzymes involved in the Leloir pathway leads to toxic accumulation of galactose, hence the term ‘galactosemia’. Classical galactosemia is the most common form of galactosemia caused by GALT deficiency. The prevalence of classical galactosemia varies greatly across different populations in the world, i.e. 1 : 10 000–1 : 20 000 live births in Ireland, 1 : 25 000-1 : 44 000 live births in the United Kingdom, 1 : 50 000 cases in the United States, 1 : 100 000 newborns in Japan, and relatively low frequency in Asian populations [1]. GALK deficiency has a high prevalence of 1 : 1600 in the Romani Gypsy population [2], but in other populations GALK and the GALE deficiency are more rare and can present with acute and life-threatening clinical signs and symptoms, typically manifested within the first few days to weeks of life after consumption of breast milk and galactose-containing formula. Clinical symptoms such as jaundice, vomiting, failure to thrive and poor feeding are commonly observed in galactosemic babies [3]. Signs and symptoms of abnormal carbohydrate metabolism, kidney and liver dysfunction including aminoaciduria, hepatomegaly, hypoglycemia and elevated blood galactose and urinary galactitol are characteristic of this disorder. Untreated galactosemia can potentially lead to neonatal death. Early diagnosis and treatment is critical and usually life-saving. However, there are long-term clinical complications, including cataracts, short stature, neurodevelopmental problems, premature ovarian failure, developmental delay and impaired cognitive functions [4].
Biochemical tests for galactosemia and their limitations
Newborn bloodspot screening (NBS) for galactosemia is not currently recommended by the United Kingdom Newborn Screening Committee because it fails to meet their strict criteria. Current tests have high false-positive rates and early treatment is only partially successful. However, galactosemia is frequently detected under the existing protocol owing to affected babies having elevated phenylalanine (≥200 µmol/L) and tyrosine (≥240 µmol/L) and so are investigated for probable liver diseases [5].
To date, a small number of laboratory tests are offered by specialist metabolic centres in the UK to aid the diagnosis and monitoring of galactosemia, including urinary sugar chromatography, the Beutler fluorescent spot test, urinary galactitol quantitation, quantitative assays of erythrocyte GALT, GALK and GALE enzymes, genetic analysis and galactose-1-phosphate (Gal-1-P) analysis (Table 1).
Urinary sugar chromatography
Increased urinary excretion of galactose, a feature of galactosemia, will give rise to a positive reducing substances result. The identification of the sugar is possible by a chromatography technique, as is the field method in the UK. These are useful first-line tests; however, false-negative results may be seen in patients who have already started a lactose-free diet.
Beutler test
Another commonly used first-line test that qualitatively detects the activity of GALT is the Beutler fluorescent spot test. This is a robust, technically simple test that works well in most situations. However, false-negative results could be expected following a blood transfusion. Also as the endogenous enzyme glucose-1-phosphate dehydrogenase (G6PD) is used as a linked enzyme in the Beutler method, G6PD deficiency will lead to a false-positive result.
GALT, GALK and GALE enzyme assay
The gold standard diagnostic tests are the quantitative assay for GALT, GALK and GALE to distinguish and confirm the three forms of galactosemia. However, blood transfusion will affect the validity of the enzyme results in the same manner as the Beutler test. Detection of the enzyme activities in lymphocytes may be helpful but all of these assays are laborious and time-consuming.
Galactose-1-phosphate (Gal-1-P) quantitation
The quantitative measurement of galactose-1-phosphate (Gal-1-P) is another technically complicated test that is useful to support the diagnosis in all forms of galactosemia. Gal-1-P has also been used as a biomarker to monitor dietary compliance in galactosemic patients; however, it is not a reliable marker for long-term monitoring because it reflects only the galactose ingestion in the past 24 hours and poorly correlates with long-term clinical outcome [6].
Urinary galactitol quantitation
Urinary galactitol, an end product of galactose formed by an alternative pathway, is invariably excreted in significant quantities in patients with all forms of galactosemia. As galactose is produced endogenously, the level of urinary galactitol is expected to be less affected by the dilutional effect of the blood transfusion or the exogenous/dietary source of galactose. In comparison to normal healthy controls, urinary galactitol excretion is significantly elevated at birth in all forms of galactosemia, including the milder phenotypes of GALT, i.e. S135L homozygosity [7] and in the Duarte variants [8]. The level of urinary galactitol decreases rapidly following commencement of dietary treatment but still remains above the reference ranges for normal healthy controls [7]. However, several studies have shown that galactitol is not correlated with dietary galactose intake or erythrocyte Gal-1-P concentration [8], nor with the development of long-term complications in patients with classical galactosemia [9]. In addition, the high intra-individual biological variability of urinary galactitol may limit its value in disease investigation and monitoring [10].
The practice in the diagnosis and monitoring of galactosemia varies widely, in particular on the use of urinary galactitol. The latest international guideline for classical galactosemia recommended that although urinary galactitol is unsuitable for disease monitoring, it could be used as a ‘supportive diagnostic test’ following blood transfusions [11], a treatment frequently used in neonatal care units. However, this test is not widely available and may be underused. Further research is necessary to evaluate the clinical usefulness of urinary galactitol in aiding the diagnosis and monitoring of galactosemia.
Measurement of urinary galactitol
Galactitol is the toxic metabolic by-product formed intracellularly following reduction of galactose by aldose reductase. Galactitol is subsequently excreted in the urine as it cannot be further oxidized by sorbitol dehydrogenase. This sugar alcohol has been extensively studied in urine, blood, amniotic fluid, liver, kidney, cardiac muscle, skeletal muscle, brain and the eye lens. Most clinically relevant data were derived from investigations on urinary galactitol. The analytical methods employed for identification and measurement of urinary galactitol have involved gas–liquid chromatography with trimethylsilyl (TMS) or methoxylamine-acetate derivatives, isotope dilution gas chromatography–mass spectrometry (GC-MS) with acetate derivative, reverse-phase high-performance liquid chromatography, thin-layer chromatography and proton magnetic resonance spectroscopy. Most research reported that GC-MS is particularly suitable for the quantitation of urinary polyols as it offers high resolution, great sensitivity and rapid analytical speed [12].
Urinary galactitol quantitation by gas chromatography–mass spectrometry
The Willink Biochemical Genetic Laboratory conducted a preliminary study on urinary galactitol quantitation by using a GC-MS method to evaluate the key analytical validation components, establish the age-related reference ranges, and to study the relationship between urinary galactitol excretion and hepatic dysfunctions. The study included plain urine samples from two known patients with galactosemia, random urine samples from eight unaffected patients with suspected hepatic dysfunction, and 120 individuals unaffected by galactosemia, received in the Willink Laboratory for a metabolic screen. The procedure was modified from the method described by Pettit et al. and Allen et al. based on the method principle of acetate derivatives formation followed by separation and detection using GC-MS [13, 14]. The method was linear from 2.5 µmol/L to 330 µmol/L. The lower limit of detection (LoD) and lower limit of quantification (LoQ) were 3 µmol/L and 9 µmol/L. Intra- and inter-assay precisions were 1.41–6.22% and 2.54–17.04% respectively at levels across the measuring range. We used a total of 27 samples from the ERNDIM (European Research Network for evaluation and improvement of screening, Diagnosis and treatment of Inherited Disorders of Metabolism) ‘Specialist Assays in Urine’ external quality assessment (EQA) scheme to test if our method was in agreement with those of other specialist laboratories. Figure 1 showed that the results from the GC-MS method were in good agreement with the method means (R2=0.944). We showed that samples for urinary galactitol measurement were stable up to 7 days under storage at −20 °C, 4 °C and room temperature. Our findings and other studies demonstrated that urinary galactitol excretion in both normal and galactosemic subjects are age-dependent, with the highest excretion at a younger age (Fig. 2). A minimal amount of galactitol can be found in urine samples of healthy individuals owing to the generation of galactose by endogenous metabolic reactions. Newborns are expected to excrete a greater amount of galactitol than older children as the neonatal liver is not yet fully developed and, thus, less effective in metabolizing the increased load of galactose after milk feeding. The age-related reference ranges were ≤85, ≤68, ≤29, ≤23, ≤9 and ≤4 µmol/mmol creatinine for the 0–3 months, 4–11 months, 1–2 years, 3–6 years, 7–15 years and >15 years age groups, respectively. In our study, galactosemic patients excreted 9-fold to ≥800-fold more urinary galactitol than the age-matched control group, whereas non-galactosemic patients with suspected hepatic dysfunction excreted 3-fold more. An elevated urinary galactitol result alone is does not identify whether galactosemia is caused by enzyme deficiency in the Leloir pathway or by other secondary causes. It is of utmost importance to consider further biochemical and radiological investigations for patients with hepatic dysfunctions and metabolic disorders in order to differentiate and confirm the diagnosis of hypergalactosemia.
Conclusion and future work
Further work is required for a comprehensive analytical and clinical validation of the test method, but our preliminary data are promising and demonstrate that the GC-MS quantitation of urinary galactitol would be acceptable for the diagnosis of galactosemia. Urinary galactitol is potentially very useful as a supportive diagnostic test following blood transfusions and its use should be encouraged. Its application as a first-line test for all forms of galactosemia is undisputable. A full evaluation of its clinical application will be possible following implementation of this assay into routine service in the Willink Biochemical Genetics Laboratory.
Acknowledgements
We would like to thank Graeme Smith and James Cooper for their technical expertise in helping to set up and validate the GC-MS assay for galactitol in our laboratory. We would also like to thank Ann Brown and the staff of the Clinical Chemistry Department at Southmead Hospital, Bristol, for sharing their in-house standard operating procedure for this method and demonstrating its use within their laboratory.
The Willink Laboratory acknowledges the use of data derived from ERNDIM EQA materials in this publication. The use of ERNDIM EQA materials does not imply that ERNDIM endorses the methods used or the scientific validity of the findings in this publication. ERNDIM (www.erndim.org) is an independent, not for profit foundation that provides EQA schemes in the field of inborn errors of metabolism with the aim of improving diagnosis, treatment and monitoring of inherited metabolic diseases.
References
1. Saleem U, Mahmood S, Kamran SH, Mutt MA, Ahmad B. Prevalence, epidemiology and clinical study of galactosemia. J App Pharm 2012; 4(1): 524–530.
2. Kalaydjieva L, Perez-Lezaun A, Angelicheva D, Onengut S, Dye D, Bosshard NU, Jordanova A, Savov A, Yanakiev P, et al. Founder mutation in the GK1 gene is responsible for galactokinase deficiency in Roma (gypsies). Am J Hum Genet 1999; 65(5): 1299–1307.
3. Waggoner DD, Buist NRM, Donnell GN. Long-term prognosis in galactosaemia: results of a survey of 350 cases. J Inherit Metab Dis 1990; 13(6): 802–818.
4. Walter JH, Collins JE, Leonard JV, Chiswick M and Marcovitch H. Recommendations for the management of galactosaemia commentary. Arch Dis Child 1999; 80(1): 93–96.
5. UK National Screening Committee. Screening for galactosaemia: external review against programme appraisal criteria for the UK National Screening Committee (UK NSC). Bazian Ltd. 2014. http://legacy.screening.nhs.uk/screening-recommendations.php.
6. Van Calcar SC, Bernstein LE, Rohr FJ, Scaman CH, Yannicelli S, Berry GT. A re-evaluation of life-long severe galactose restriction for the nutrition management of classic galactosemia. Mol Genet Metab 2014; 112(3): 191–197.
7. Palmieri M, Mazur A Berry GT Ning C, Wehrli S, Yager C, Reynolds R, Singh R, Muralidharan K, et al. Urine and plasma galactitol in patients with galactose-1-phosphate uridyltransferase deficiency galactosaemia. Metabolism 1999; 48: 1294–1302.
8. Ficicioglu C, Hussa C, Gallagher PR, Thomas N, Yager C. Monitoring of biochemical status in children with Duarte galactosemia: utility of galactose, galactitol, galactonate, and galactose 1-phosphate. Clin Chem 2010; 56(7): 1177–1182.
9. Cleary MA, Heptinstall LE, Wraith JE, Walter JH. Galactosaemia: relationshiop of IQ to biochemical control and genotype. J Inherit Metab Dis 1995; 18, 151–152.
10. Hutcheson ACJ, Murdoch-Davis C, Green A, Preece MA, Allen J, Holton JB, Rylance G. Biochemical monitoring of treatment for galactosaemia: biological variability in metabolite concentrations. J Inherit Metab Dis 1999; 22(2): 139–148.
11. Welling L Bernstein LE, Berry GT, Burlina AB, Eyskens F, Gautschi M, Grünewald S, Gubbels CS, Knerr I, et al. International clinical guideline for the management of classical galactosaemia: diagnosis, treatment, and follow-up. J Inherit Metab Dis 2017; 40(2): 171–176.
12. Haga H, Nakajima T. Determination of polyol profiles in human urine by capillary gas chromatography. Biomed Chromatogr 1989; 3(2): 68–71.
13. Pettit BR, King GS, Blau K. The analysis of hexitols in biological fluid by selected ion monitoring. Biomed Mass Spectrom 1980; 7(7): 309–313.
14. Allen JT, Holton JB, Lennox AC, Hodges IC. Early morning urine galactitol levels in relation to galactose intake: A possible method of monitoring the diet in galactokinase deficiency. J Inherit Metab Dis 1988; 11(S2): 243–245.
The authors
Yuh Luan Choo1 MSc; Teresa Hoi-Yee Wu2 MSc, FRCPath; Jackie Till2 BSc; Mick Henderson*2 PhD, FRCPath
1Faculty of Medical and Human Science, University of Manchester, Manchester
M13 9PL, UK
2Willink Biochemical Genetics Laboratory, Manchester, Manchester M13 9PL, UK
*Corresponding author
E-mail: Mick.henderson@nhs.net