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
Rapid and reliable detection of medulloblastoma-associated DNA methylation patterns: MS-MIMIC
, /in Featured Articles /by 3wmediaDisease-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
Point-of-care glucose meters: useful in a neonatal setting
, /in Featured Articles /by 3wmediaPoint-of-care glucose meters are used in a variety of settings to monitor glucose concentration in whole blood. Comparability between the results from these meters and results issued on plasma samples was examined by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), which in 2006 recommended that all glucose results should be reported as a plasma concentration. The group advised that a conversion factor of 1.11 be used to convert whole blood results to plasma equivalence. As neonatal hematocrit differs from that seen in adults, the IFCC recommendation is not appropriate in neonatal samples. It was decided to review this recommendation.
by Mary Stapleton and Ruth O’Kelly
Introduction
Neonates may be at risk of hypoglycemia in the first few hours and days after birth, the cause of which may be attributed to the stress of extra-uterine life [1]. However, it may also signal an underlying pathology, and prolonged episodes of hypoglycemia have been described as a cause of neurodevelopmental morbidity [2]. Identification of hypoglycemic episodes is, therefore, considered to be vital in the neonatal period, but the population in question often includes extremely premature and small infants. By regularly using point-of-care (POC) devices to measure glucose in this cohort of patients, it is hoped to obtain useful results while avoiding unnecessary blood loss.
In instances where glucose results obtained on POC devices do not fit the clinical picture, a fluoride-preserved sample may be sent for plasma analysis.
Discrepancies between POC whole blood and laboratory plasma results may be a cause of lack of confidence in bedside technology. There are several causes of such discrepancies, and while literature has suggested that hypoglycemia is missed by using POC devices, the role of glycolysis as a pre-analytical factor is starting to be recognized [3]. The second possible cause is that differing sample types are measured and unlikely to be comparable. In 2006, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) published a recommendation that manufacturers of POC devices were to report glucose concentration as though it were a plasma sample rather than whole blood. A conversion factor of 1.11 was calculated to equate the results from the two sample types (whole blood × 1.11 = plasma) [4].
The aim of this study was to perform glucose measurements in neonatal and adult whole blood and plasma samples by a laboratory method and a POC method without a plasma correction factor. By comparing results, it was hoped to determine the appropriateness of the plasma conversion factor as recommended by the IFCC.
Methods
The HemoCue 201+ POC methodology that was used to analyse whole blood samples consists of an analyser and measuring cuvette containing dried reagents. The cuvette serves as a pipette, reaction chamber and measuring vessel. Analysis of plasma for glucose concentration was performed on an automated chemistry platform (Beckman Coulter AU640) using a hexokinase method in a laboratory accredited to ISO 15189 standards.
Samples for plasma glucose analysis were obtained in tubes containing fluoride as an antiglycolytic agent. When measuring glucose in the POC device, an aliquot of sample was taken from the sample in the blood tube before separation.
Statistical analysis, using Bland–Altman analysis to compare results by two different methods, was performed using Analyse-It software for Microsoft Excel (Analyse-It Software Ltd).
Study 1
Fluoride-stabilized plasma samples from 25 neonates (aged 3 days or less) received into the laboratory for routine glucose estimation were included in the study. An aliquot was taken from each sample before centrifugation and analysis, and glucose determination by POC was performed on a HemoCue 201+ analyser located in the laboratory.
Study 2
Fluoride plasma samples from pregnant women (n = 34) were also analysed for whole blood and plasma glucose in the same manner described in study 1.
Study 3
A portion of patients who were a part of the study had a sample sent for full blood count (FBC) analysis on the same day of the glucose request. Results were subdivided into greater and less than the median result for both hematocrit and mean corpuscular volume (MCV). These were then reviewed against the reported glucose concentrations.
Results
Studies 1 and 2
No significant difference was noted between neonatal samples analysed (Table 1, Fig. 1) (bias, 0.05mmol/L). However, a significant difference (P<0.0001) was noted between the two methods when samples had been obtained from adult patients (Table 2, Fig. 2) (bias, 0.6mmol/L).
Study 3
A standard calculation for determining the percentage of water in blood was reviewed (Equation 1). The data obtained from the FBC samples was used to propose plasma conversion factors for both adult and neonatal patients (Table 3). It was assumed that the median hematocrit in a healthy, non-pregnant adult is 0.43 L/L, with a resulting calculated conversion factor (CCF) of 1.11.
Discussion
This study investigated the reported difference between samples analysed for glucose using POC meters in a ward setting and those samples received for glucose analysis in a central laboratory. It may be seen that there is good correlation between POC and laboratory analyser methods in samples obtained from neonates.
This correlation was not seen in the set of adult samples analysed, and an average difference of up to 10% in results was reported from the two methods. By applying a plasma equivalence factor of 1.11 to the whole blood results from adults as recommended by the IFCC in 2006, the difference in results from adult patients could be explained.
The IFCC equivalence factor based on the hematocrit in neonates is 1.15, but this study confirms that the neonatal samples did not require this factor. POC glucose measurements in the HemoCue device include a cell lysis step and thus whole blood (intra-and extra-cellular) glucose is measured. However, neonatal blood is recognized as containing resistant cells and cells may not fully lyse causing the measured glucose to reflect extra-cellular glucose similar to plasma measurements.
In a previous study [5], Vadasdi and Jacobs compared heparinized samples from neonates that were analysed on the HemoCue immediately before centrifugation and assayed by the laboratory method. No significant difference was found between the mean values of the two methods over a hematocrit range of 0.185–0.72. Our study agrees with these findings.
Vadasdi and Jacobs suggested that the effect of hematocrit was decreased significantly by the hemolysis step in the cuvette. It is recognized that HemoCue POC meters are not affected by hematocrit [4, 5], which is why this meter is frequently used in a neonatal setting. Vadasdi and Jacobs also suggested that because the MCV (which describes the size of the red cells) is greater than seen in adults, there is less of a dilutional effect due to membrane proteins after lysis. Our study showed that the mean MCV in neonates was greater than seen in our adult (pregnant) subjects.
Conclusion
Laboratory measurements for glucose are usually performed on plasma samples while POC measurements are performed on whole blood. A difference in results may be expected as whole blood glucose is known to be approximately 11% lower than plasma glucose due to lower volume of water in the erythrocytes.
The difference between plasma and whole blood glucose in adults was similar to the recommended IFCC “plasma equivalent factor” of 1.11. The lack of difference between plasma and whole blood glucose in neonatal samples may be explained by the increased MCV or the presence of resistant red cells that may not undergo lysis in the POC device.
Many modern POC devices for measuring glucose now include the IFCC plasma conversion factor and such results should be carefully interpreted.
References
1. World Health Organization. Hypoglycaemia of the newborn. Review of the literature. WHO/CHD/97.1, 1997.
2. Lucas A, Morley R, Cole TJ. Adverse neurodevelopmental outcome of moderate neonatal hypoglycaemia. BMJ 1988; 297(6659): 1304–1308.
3. Stapleton M, Daly N, O’Kelly R, Turner MJ. Time and temperature affect glycolysis in blood samples regardless of fluoride- based preservatives: a potential underestimation of diabetes. Ann Clin Biochem 2017; 54: 671–676.
4. D’Orazio P, Burnett RW, Fogh-Anderson N, Jacobs E, Kuwa K, Külpmann WR, Larsson L, Lewenstam A, Maas AH, et al. Approved IFCC recommendation on reporting results for blood glucose: International Federation of Clinical Chemistry and Laboratory Medicine Scientific Division, Working Group on Selective Electrodes and Point of Care Testing (IFCC-SD-WG-SEPOCT). Clin Chem Lab Med 2006; 44: 1486–1490.
5. Vadasdi E, Jacobs E. HemoCue β-glucose photometer evaluated for use in a neonatal intensive care unit. Clin Chem 1993; 39(11): 2329–2332.
The authors
Mary Stapleton* FRCPath; Ruth O’Kelly FRCPath
Biochemistry Department, Coombe Women & Infants University Hospital, Dublin, Ireland
*Corresponding author
E-mail: mary.stapleton@nhs.net
Do point-of-care cardiac troponin assays perform sufficiently well to achieve clinical guidelines to rule in or to rule out acute myocardial infarction?
, /in Featured Articles /by 3wmediaCurrent emergency department strategies are aimed at reliably excluding myocardial infarction as soon as possible through clinical assessment and time-dependent measurement of high-sensitivity cardiac troponin. Point-of-care cardiac troponin methods have evolved, but can they be used to support the early rule-in or rule-out strategies for myocardial infarction?
by Dr Martha E. Lyon and Dr Andrew W. Lyon
Introduction
Significant attention has recently focused on early rule-in and rule-out strategies to detect non-ST-segment elevation (NSTEMI) acute myocardial infarction in the emergency department (ED) [1]. In 2015, the European Society of Cardiology (ESC) introduced guidelines for the management of acute coronary syndrome in patients without ST-segment elevation [2]. This guideline included interpretative algorithms to rule in or rule out acute myocardial infarction (AMI) based on clinical symptoms, high-sensitivity cardiac troponin (hs-cTn) concentrations at specific thresholds and changes in hs-cTn over intervals of 1 or 3 hours (Fig. 1) [2]. Importantly, the guidelines also highlighted the time-dependent uncertainty of using low concentration cut-offs in patients presenting early after the onset of pain with the following comment, “Only applicable if chest pain onset <3 h.” hs-cTn methods used in hospital clinical laboratories are expected to have an imprecision of ≤10% at the 99th percentile of a healthy population and allow for the detection of at least 50% and ideally >95% of healthy individuals [3]. The analytical qualities of the high-sensitivity methodology enable excellent diagnostic performance, that being a 99% clinical sensitivity and negative predictive value. However, it should be acknowledged that the prevalence of AMI in a specific population and the clinical sensitivity of the cardiac troponin test will influence the calculation of the negative predictive value [1]. Physicians will need to confirm that clinical trial populations are representative of their local population in order to verify the applicability of the diagnostic performance of the hs-cTn method [1].
Many studies with hs-cTn methods have investigated the derivation of upper reference limits, rates of change in hs-cTn concentration to detect AMI, assay imprecision at the 99th percentile concentration and performance characteristics of commercial assays with various interpretative thresholds [2, 4, 5]. Additional factors such as hemolysis, anticoagulant-bias, and within-subject variation will cause bias and imprecision in method results [6, 7]. An understanding of the interaction between these factors is incomplete because of the limited sample size in many clinical studies and the poor correlation between commercial assays [8]. In an initial attempt to understand this complex and complicated interaction, we used computer simulation models to predict the influence of method bias and imprecision on the rates of misclassification at low interpretative thresholds to rule out AMI and at thresholds near or exceeding the overall 99th percentile to rule in AMI. We found that at low thresholds, only method bias and not imprecision influenced the rate of misclassification whereas both method bias and imprecision would affect the early rule-in for AMI [9].
Point-of-care (POC) cardiac troponin devices
Short turnaround testing (STAT) in central hospital laboratories commonly employs the expectation of 1-hour turnaround time once the specimen has arrived in the laboratory. The ability to consistently meet the earlier time points, as outlined in the guideline algorithms, will represent a logistic challenge for many hospitals. POC methods provide an appealing alternative to central laboratory assessment, in particular for the initial rule-in when elevated cTn levels are present at Time 0 hr as well as sequential monitoring. However, prior to implementing a POC troponin method, comparison studies between the POC method and central laboratory cardiac troponin methods need to be conducted to assure concordance of the results. Several studies have reported a significant gap in the analytical sensitivity between hs-cTn and POC troponin methods [10, 11]. In 2015, Amundson and Apple described the analytical performance characteristics of POC cardiac troponin methods from nine different manufacturers [12]. These characteristics included clinical sensitivity and specificity, analytical imprecision, specimen type and preparation as well as the method principle of analysis. Although each of the devices provided different qualities, it was determined that an imprecision of ≤20% at the 99th percentile was paramount to limit both false-positive and false-negative results [12].
Accurate and precise measurement of cardiac troponin is essential for the consistent identification of NSTEMI patients with acute coronary syndrome. Currently, significant variation exists between clinical laboratory hs-cTn methods that could influence the clinical care provided to patients [8]. This also represents a challenge to the adoption of POC cTn technology.
Simulation models
Computer simulation model utility has been demonstrated with investigations of the impact of method bias and imprecision on potential clinical risk of insulin dosing errors with glucose meters [13], warfarin dosing errors with POC international normalized ratio (INR) devices [14] and with early rule-in and rule-out of AMI misclassification rates with cTn methods [9]. Clinical studies are challenging to conduct with NSTEMI patients presenting early in an emergency department because of variation in disease prevalence, poor correlation between cTn analytical methods and uncertainty in the time of pain onset. Low prevalence and variation in disease over time are common problems with other clinical laboratory biomarkers such as anti-viral antibodies as well as first-trimester pregnancy screening methods [15, 16]. One solution to this concern has been to generate finite mixture models of biomarker distributions to predict biomarker assay performance [17]. These simulated databases have been used to understand the relationship between clinical risk and assay characteristics and to extend use of the statistical information gathered in small clinical trials.
Simulation model investigation of the utility of POC cardiac troponin testing
Recognizing the lack of hs-cTn method standardization, the low incidence of NSTEMI AMI and the high cost of conducting clinical trials, few studies have assessed the utility of POC cardiac troponin methods to rule-in or rule-out AMI. To overcome these limitations, we recently used a simulation model to predict the diagnostic performance of two POC troponin methods (Radiometer AQT90 and Roche cobas h 232) relative to a hs-cTnT method (Roche cobas 6000/Elecsys) using an Emergency Department patient database proportionately expanded to n=10 000 in a finite mixture model. This study was presented at the 2017 Annual Conference for the American Association of Clinical Chemistry and Canadian Society of Clinical Chemists [18]. Finite mixture analysis of the 0-hr data obtained from the ROMI trial (n=1137 Optimal Troponin Cut-Offs for acute coronary syndrome by Roche hs-cTnT) enabled derivation of a simulation data set (n=10 000) troponin test results. Published regression equations were used to convert the hs-cTNT results into simulated AQT90 and h 232 cTnT results [19, 20]. Clinical sensitivity, specificity, positive and negative predicative values were calculated using the simulated hs-cTnT, AQT90 and h 232 data for AMI diagnosis using the limit of detection for the assays (Table 1).
The Roche hs-cTnT in this simulated data set achieved both sensitivity and negative predictive values above 99%. The predicted performance of the Radiometer AQT90 cTnT POC assay approached the estimates for hs-cTnT, suggesting POC methods are emerging that could be used both for AMI rule-in and AMI rule-out. The high limit of detection of the h 232 POC method limited its sensitivity and negative predictive values.
Conclusions
Rapid and reliable measurements of cardiac troponins are ongoing analytical challenges in laboratory medicine because this clinical tool is being used both to rule in and rule out NSTEMI. Clinical trials will be required to prospectively measure the diagnostic utility of high-sensitivity central laboratory and novel POC cTn methods, but simulation studies provide useful predictions. Our recent simulation study predicted we are now in an age where POC cTn methods are approaching analytical performance necessary to effectively rule in and rule out NSTEMI.
References
1. Morrow DA. Clinician’s guide to early rule-out strategies with high sensitivity cardiac troponin. Circulation 2017; 135: 1612–1616.
2. Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, Bax JJ, Borger MA, Brotons C et al. 2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 2016; 37: 267–315.
3. Jarolim P. High sensitivity cardiac troponin assays in the clinical laboratories. Clin Chem Lab Med 2015; 53: 635–652.
4. Wildi K, Gimenez MR, Twerenbold R, Reichlin T, Jaeger C, Heinzelmann A, Arnold C, Nelles B, Druey S et al. Misdiagnosis of myocardial infarction related to limitations of the current regulatory approach to define clinical decision values for cardiac troponin. Circulation 2015; 131: 2032–2040.
5. Love SA, Sandoval Y, Smith SW, Nicholson J, Cao J, Ler R, Schulz K, Apple FS. Incidence of undetectable, measurable, and increased cardiac troponin I concentrations above the 99th percentile using a high-sensitivity vs a contemporary assay in patients presenting to the emergency department. Clin Chem 2016; 62: 1115–1119.
6. Krintus M, Kozinski M, Boudry P, Capell NE, Koller U, Lackner K, Lefèvre G, Lennartz L, Lotz J et al. European multicenter analytical evaluation of the Abbott ARCHITECT STAT high sensitive troponin I immunoassay. Clin Chem Lab Med 2014; 52: 1657–1665.
7. Ryan JB, Wallace J, Sies CV, Florkowski CM, George PM. Evaluation of Abbott Architect high-sensitivity troponin I assay for haemolysis interference. Pathology 2015; 47: 716–718.
8. Ungerer JPJ, Tate J, Pretorius JC. Discordance with 3 cardiac troponin I and T assays: implications for the 99th percentile cut-off. Clin Chem 2016; 62: 1106–1114.
9. Lyon AW, Kavsak P, Lyon OAS, Worster A, Lyon ME. Simulation models of misclassification error for single thresholds of high-sensitivity cardiac troponin I due to assay bias and imprecision. Clin Chem 2017; 63: 585–592.
10. Palamalai V, Murakami MM, Apple FS. Diagnostic performance of four point of care troponin I assays to rule in and rule out acute myocardial infarction. Clin Biochem 2013; 46: 1631–1635.
11. Bruins Slot MHE, van der Heijden GJMG, Stelpstra SD, Hoes AW, Rutten FH. Point-of-care tests in suspected acute myocardial infarction: A systematic review. Int J Cardiol 2013; 168: 5255–5262.
12. Amundson BE, Apple FS. Cardiac troponin assays: a review of quantitative point-of-care devices and their efficacy in the diagnosis of myocardial infarction. Clin Chem Lab Med 2015; 53: 665–676.
13. Karon BS, Boyd JC, Klee GG. Glucose meter performance criteria for tight glycemic control estimated by simulation modeling. Clin Chem 2010; 56: 1091–1097.
14. Lyon ME, Sinha R, Lyon OAS, Lyon AW. Application of a simulation model to estimate treatment error and clinical risk derived from point-of-care INR device analytic performance. J Appl Lab Med 2017; 2: 25–32.
15. Harelip P, Williams D, Dezateux C, Tookey PA, Peckham CS. Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models. Epidemiol Infect 2008; 136: 1698–1706.
16. Wright D, Abele H, Baker A, Kagan KO. Impact of bias in serum free beta-human chorionic gonadotroponin and pregnancy-associated plasma protein-A multiples of the median levels on first-trimester screening of trisomy 21. Ultrasound Obstet Gynecol 2011; 38: 309–313.
17. Deb P, Trivedi PK. Demand for medical care by the elderly: a finite mixture approach. J Appl Econ 1997; 12: 313–326.
18. Lyon ME, Kavsak PA, Worster A, Lyon AW. Simulation models to rule out acute myocardial infarction with two point-of-care testing devices and a high sensitivity cardiac troponin T method. Poster Presentation. AACC/CSCC Annual Meeting, San Diego, CA, USA, 2017.
19. Bertsch T, Chapelle JP, Dempfle CE, Giannitsis E, Schwab M, Zerback R. Multicentre analytical evaluation of a new point-of-care system for the determination of cardiac and thromboembolic markers. Clin Lab 2010; 56: 37–49.
20. Le Goff C, Evrards S, Brevers E, Kaux JF, Cavalier E. Evaluation of troponin T on AQT90 and Cobas 8000 as a rule-in/-out tool in an emergency ward. Poster Presentation, EuroMed Lab Conference 2014.
The authors
Martha E. Lyon* PhD, DABCC, FACB and Andrew W. Lyon PhD
Department of Pathology & Laboratory Medicine, Division of Clinical Biochemistry, Saskatoon Health Region, Saskatoon, Saskatchewan, Canada
*Corresponding author
E-mail: martha.lyon@saskatoonhealthregion.ca
Scientific literature review: Kidney disease markers
, /in Featured Articles /by 3wmediaBiomarkers of diabetic nephropathy: A 2017 update
Papadopoulou-Marketou N, Kanaka-Gantenbein C, Marketos N, Chrousos GP, Papassotiriou I. Crit Rev Clin Lab Sci 2017; 54(5): 326–342
Diabetic nephropathy (DN), also named diabetic kidney disease (DKD), is a devastating complication in patients with both type 1 and 2 diabetes mellitus (T1D and T2D) and its diagnosis has been traditionally based on the presence of micro-albuminuria (MA). The aim of this article is to update, through review of the relevant medical literature, the most promising biomarkers for early DKD detection. MA has historically been employed as an early marker of microvascular complications, indicating risk for advanced CKD. However, due to the inability of MA to adequately predict DKD, especially in young patients or in non-albuminuric DKD, additional biomarkers of glomerular and/or tubular injury have been proposed to uncover early renal dysfunction and structural lesions, even before MA occurs. Defining new predictive biomarkers to use alongside urinary albumin excretion (UAE) during the initial stages of DKD would provide a window of opportunity for preventive and/or therapeutic interventions to prevent or delay the onset of irreversible long-term complications and to improve outcomes by minimizing the rates of severe cardio-renal morbidity and mortality in DKD patients.
Urinary angiotensinogen and renin excretion are associated with chronic kidney disease
Juretzko A, Steinbach A, Hannemann A, Endlich K, Endlich N et al. Kidney Blood Press Res 2017; 42(1): 145–155
BACKGROUND/AIMS: Several studies sought to identify new biomarkers for chronic kidney disease (CKD). As the renal renin-angiotensin system is activated in CKD, urinary angiotensinogen or renin excretion may be suitable candidates. We tested whether urinary angiotensinogen or renin excretion is elevated in CKD and whether these parameters are associated with estimated glomerular filtration rate (eGFR). We further tested whether urinary angiotensinogen or renin excretion may convey additional information beyond that provided by albuminuria.
METHODS: We measured urinary and plasma angiotensinogen, renin, albumin and creatinine in 177 CKD patients from the Greifswald Approach to Individualized Medicine project and in 283 healthy controls from the Study of Health in Pomerania. The urinary excretion of specific proteins is given as protein-to-creatinine ratio. Receiver operating characteristic (ROC) curves, spearman correlation coefficients and linear regression models were calculated.
RESULTS: Urinary angiotensinogen [2 511 (196–31 909) vs 18.6 (8.3–44.0) pmol/g, *P<0.01] and renin excretion [0.311 (0.135–1.155) vs 0.069 (0.045–0.148) pmol/g, *P<0.01] were significantly higher in CKD patients than in healthy controls. The area under the ROC curve was significantly larger when urinary angiotensinogen, renin and albumin excretion were combined than with urinary albumin excretion alone. Urinary angiotensinogen (ß-coefficient −2.405, standard error 0.117, P<0.01) and renin excretion (ß-coefficient −0.793, standard error 0.061, P<0.01) were inversely associated with eGFR. Adjustment for albuminuria, age, sex, systolic blood pressure and body mass index did not significantly affect the results.
CONCLUSION: Urinary angiotensinogen and renin excretion are elevated in CKD patients. Both parameters are negatively associated with eGFR and these associations are independent of urinary albumin excretion. In CKD patients urinary angiotensinogen and renin excretion may convey additional information beyond that provided by albuminuria.
KIM-1 Is a potential urinary biomarker of obstruction: results from a prospective cohort study
Olvera-Posada D, Dayarathna T, Dion M, Alenezi H, Sener A et al. J Endourol 2017; 31(2): 111–118
INTRODUCTION: Partial or complete obstruction of the urinary tract is a common and challenging urological condition that may occur in patients of any age. Serum creatinine is the most commonly used method to evaluate global renal function, although it has low sensitivity for early changes in the glomerular filtration rate or unilateral renal pathology. Hence, finding another measurable parameter that reflects the adaptation of the renal physiology to these circumstances is important. Several recent studies have assessed the use of new biomarkers of acute kidney injury (AKI), but the information among patients with stone disease and those with obstructive uropathy is limited.
MATERIAL AND METHODS: A prospective cohort study was conducted to determine the urinary levels of kidney injury molecule-1 (KIM-1), Total and Monomeric neutrophil gelatinase-associated lipocalin (NGAL) in patients with hydronephrosis secondary to renal stone disease, congenital ureteropelvic junction obstruction or ureteral stricture. Comparison between patients with hydronephrosis and no hydronephrosis was carried out along with correlation analysis to detect factors associated with biomarker expression.
RESULTS: Urinary levels of KIM-1 significantly decreased after hydronephrosis treatment in patients with unilateral obstruction (1.19 ng/mL vs 0.76 ng/mL creatinine, P=0.002), additionally KIM-1 was significantly higher in patients with hydronephrosis compared to stone disease patients without radiological evidence of obstruction (1.19 vs 0.64, P=0.006). Total and Monomeric NGAL showed a moderate correlation with the presence of leukocyturia. We found that a KIM-1 value of 0.735 ng/mg creatinine had a sensitivity of 75% and specificity of 67% to predict the presence of hydronephrosis in preoperative studies (95% CI 0.58-0.87, P = 0.006).
CONCLUSION: Our results show that KIM-1 is a promising biomarker of subclinical AKI associated with hydronephrosis in urological patients. NGAL values were influenced by the presence of leukocyturia, limiting its usefulness in this population.
Heparin-binding protein (HBP) improves prediction of sepsis-related acute kidney injury
Tverring J, Vaara ST, Fisher J, Poukkanen M et al. Ann Intensive Care 2017; 7(1): 105
BACKGROUND: Sepsis-related acute kidney injury (AKI) accounts for major morbidity and mortality among the critically ill. Heparin-binding protein (HBP) is a promising biomarker in predicting development and prognosis of severe sepsis and septic shock that has recently been proposed to be involved in the pathophysiology of AKI. The objective of this study was to investigate the added predictive value of measuring plasma HBP on admission to the intensive care unit (ICU) regarding the development of septic AKI.
METHODS: We included 601 patients with severe sepsis or septic shock from the prospective, observational FINNAKI study conducted in seventeen Finnish ICUs during a 5-month period (1 September 2011-1 February 2012). The main outcome measure was the development of KDIGO AKI stages 2–3 from 12 h after admission up to 5 days. Statistical analysis for the primary endpoint included construction of a clinical risk model, area under the receiver operating curve (ROC area), category-free net reclassification index (cfNRI) and integrated discrimination improvement (IDI) with 95% confidence intervals (95% CI).
RESULTS: Out of 511 eligible patients, 101 (20%) reached the primary endpoint. The addition of plasma HBP to a clinical risk model significantly increased ROC area (0.82 vs 0.78, P=0.03) and risk classification scores: cfNRI 62.0% (95% CI 40.5–82.4%) and IDI 0.053 (95% CI 0.029–0.075).
CONCLUSIONS: Plasma HBP adds predictive value to known clinical risk factors in septic AKI. Further studies are warranted to compare the predictive performance of plasma HBP to other novel AKI biomarkers.
Prediction of contrast induced acute kidney injury using novel biomarkers following contrast coronary angiography
Connolly M, Kinnin M, McEneaney D, Menown I et al. QJM. 2017; doi: 10.1093/qjmed/hcx201
BACKGROUND: Chronic kidney disease (CKD) is a risk factor for contrast-induced acute kidney injury (CI-AKI). Contrast angiography in CKD patients is a common procedure. Creatinine is a delayed marker of CI-AKI and delays diagnosis which results in significant morbidity and mortality.
AIM: Early diagnosis of CI-AKI requires validated novel biomarkers.
DESIGN: A prospective observation study of 301 consecutive CKD patients undergoing coronary angiography was performed. Samples for plasma neutrophil gelatinase-associated lipocalin (NGAL), serum liver fatty acid-binding protein (L-FABP), serum kidney injury marker 1 (KIM-1), serum interleukin 18 (IL-18) and serum creatinine were taken at 0, 1, 2, 4, 6 and 48 hours post contrast. Urinary NGAL and urinary cystatin C (CysC) were collected at 0, 6 and 48 hours. Incidence of major adverse clinical events (MACE) were recorded at 1 year. CI-AKI was defined as an absolute delta rise in creatinine of ≥26.5µmol/L or a 50% relative rise from baseline at 48 hours following contrast.
RESULTS: CI-AKI occurred in 28 (9.3%) patients. Plasma NGAL was most predictive of CI-AKI at 6 hours. L-FABP performed best at 4 hours.A combination of Mehran score >10, 4-hour L-FABP and 6-hour NGAL improved specificity to 96.7%. MACE was statistically higher at one year in CI-AKI patients (25.0% versus 6.2% in non CI-AKI patients).
CONCLUSIONS: Mehran risk score, 4 hour serum L-FAPB and 6 hour plasma NGAL performed best at early CI-AKI prediction. CI-AKI patients were four times more likely to develop MACE and had a trebling of mortality risk at 1 year.
Upregulation of long noncoding RNA PVT1 predicts unfavorable prognosis in patients with clear cell renal cell carcinoma
Bao X, Duan J, Yan Y, Ma X et al. Cancer Biomark 2017; doi: 10.3233/CBM-170251
BACKGROUND: Renal cell carcinoma (RCC) is one of the most malignant genitourinary diseases worldwide. Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs in the human genome that are involved in RCC initiation and progression.
OBJECTIVE: To investigate the expression of PVT1 in ccRCC and evaluate its correlation with clinicopathologic characteristics and patients’ survival.
METHODS: Quantitative real-time PCR was performed to examine PVT1 expression in 129 ccRCC tissue samples and matched adjacent normal tissue samples. The relationship of PVT1 expression with clinicopathologic characteristics and clinical outcome was evaluated.
RESULTS: We identified the lncRNA PVT1, which was upregulated in clear cell renal cell carcinoma (ccRCC) tissues when compared with corresponding controls. Furthermore, PVT1 expression was positively associated with gender, tumor size, pT stage, TNM stage, and Fuhrman grade. Kaplan-Meier survival analysis showed that patients with high PVT1 expression had shorter disease-free survival (DFS) and overall-survival (OS) than those with low PVT1 expression, and multivariate analysis identified PVT1 as an independent prognostic factor in ccRCC.
CONCLUSIONS: PVT1 may be an oncogene as well as may promote metastasis in ccRCC and could serve as a potential biomarker to predict the prognosis of ccRCC patients.
Urine S100 proteins as potential biomarkers of lupus nephritis activity
Turnier JL, Fall N, Thornton S, Witte D et al.
BACKGROUND: Improved, non-invasive biomarkers are needed to accurately detect lupus nephritis (LN) activity. The purpose of this study was to evaluate five S100 proteins (S100A4, S100A6, S100A8/9, and S100A12) in both serum and urine as potential biomarkers of global and renal system-specific disease activity in childhood-onset systemic lupus erythematosus (cSLE).
METHODS: In this multicentre study, S100 proteins were measured in the serum and urine of four cSLE cohorts and healthy control subjects using commercial enzyme-linked immunosorbent assays. Patients were divided into cohorts on the basis of biospecimen availability: (1) longitudinal serum, (2) longitudinal urine, (3) cross-sectional serum, and (4) cross-sectional urine. Global and renal disease activity were defined using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and the SLEDAI-2K renal domain score. Nonparametric testing was used for statistical analysis, including the Wilcoxon signed-rank test, Kruskal-Wallis test, Mann-Whitney U test, and Spearman’s rank correlation coefficient.
RESULTS: All urine S100 proteins were elevated in patients with active LN compared with patients with active extrarenal disease and healthy control subjects. All urine S100 protein levels decreased with LN improvement, with S100A4 demonstrating the most significant decrease. Urine S100A4 levels were also higher with proliferative LN than with membranous LN. S100A4 staining in the kidney localized to mononuclear cells, podocytes, and distal tubular epithelial cells. Regardless of the S100 protein tested, serum levels did not change with cSLE improvement.
CONCLUSIONS: Higher urine S100 levels are associated with increased LN activity in cSLE, whereas serum S100 levels do not correlate with disease activity. Urine S100A4 shows the most promise as an LN activity biomarker, given its pronounced decrease with LN improvement, isolated elevation in urine, and positive staining in resident renal cells.
An evaluation of the AST:ALT ratio in identifying patients in primary care for appropriate referral to gastroenterology
, /in Featured Articles /by 3wmediaThe need to differentiate patients with advanced liver disease from those with earlier stage, or more benign diseases for optimal management and allocation of resources is an ever present challenge. In this article we discuss our experiences of using the aspartate transaminase (AST) : alanine transaminase (ALT) ratio as part of a pathway to screen patients for referral to secondary care.
by Dr Raphael Buttigieg and Dr Sara Jenks
Introduction
Deaths from liver disease in Scotland are on the increase [1]. More often than not patients are picked up at a late stage of their disease with significant fibrosis and/or cirrhosis already present. As a result there is a need to try to identify patients with progressive disease earlier on in the course of their illness.
Abnormal liver function tests (LFTs) are frequently picked up on general screening blood samples done in primary care. The degree of abnormality correlates poorly with the extent of liver disease. The gold standard test for liver disease diagnosis and staging is considered to be a liver biopsy; however, there are many other considerations to this invasive procedure including clinical risk, technical ability of person doing the biopsy, inter-pathologist variation in scoring and others. These limitations have led to the development of non-invasive methods for the assessment of liver fibrosis. Although there have been suggestions by different groups regarding the appropriate use of non-invasive fibrosis scoring systems, no one guideline is currently in use.
Non-invasive methods rely on two different approaches [2]:
(a) A biomarker-based approach using serum samples. Advantages are their high applicability (>95%) and good inter-laboratory reproducibility.
(b) A physical approach based on the measurement of liver stiffness. Liver stiffness corresponds to an intrinsic physical property of liver parenchyma. Physical approaches include transient elastography such as FibroScan® and MR elastography.
Because of noted variations in care, as well as to ensure appropriate referrals, NHS Lothian made a guideline for GPs in 2013. At the time, based on the best available clinical evidence, the aspartate transaminase (AST) : alanine transaminase (ALT) ratio was chosen as a scoring system to guide referrals, which was developed in recognition that as liver fibrosis develops, the normal ratio tends to reverse. An abnormal AST:ALT ratio can, thus, be used to pick up patients who should be referred to secondary care for further investigation, as well as closer monitoring and treatment.
However, other biomarker-based fibrosis risk scores have also been developed [2], which have been used for this purpose including the Fibrosis-4 (FIB-4) [3], NAFLD (non-alcoholic fatty liver disease) fibrosis score, and APRI (AST to platelet ratio index), which may have a better performance than the AST:ALT ratio [4]. Each of these has been validated for different liver diseases – and in many cases different cut-off points are recommended for diagnosis of advancing fibrosis based on the likely primary pathology involved in the individual patient. For example, alcohol use in itself will raise the AST and, thus, the same AST:ALT ratio is likely to indicate more advanced fibrosis in someone with HCV-related liver disease than in alcoholic liver disease with ongoing ethanol excess. This adds to the complexity of using any one score in a guideline to ensure the right balance between sensitivity and specificity.
A final consideration to note is that specifically for NAFLD/non-alcoholic steatohepatitis (NASH), the continued development of pharmaceuticals for the prevention of disease progression means that, once again, the threshold for diagnosis may need to change as therapies to target earlier stages become available [5].
This article will discuss our guideline (Fig. 1) and conclusions drawn from an audit of its use.
Method
A list of all the requests for a AST:ALT ratio in a 6-month period in NHS Lothian was obtained from laboratory records – in terms of date of request, patient name and CHI number (unique patient identifier) (n=874). These records were encoded into a spreadsheet and a plan for analysis made.
Following this, various data were audited retrospectively from the patient electronic record. Individual notes and files were not used because of the logistical difficulty in analysing large numbers of case notes.
Of the total number of ratios (n=874) requested in the 6-month period, 49 were elevated at >1.0 and 295 were normal at ≤1.0; 530 ratio requests were cancelled due to ALT being within the reference range.
Results
The various aspects of the referral process from primary to secondary care were audited with the following aims.
1. To identify all the abnormal ratios in a 6-month period (n=49) (Table 1).
2. To identify all the ratios in the same 6-month period that were in the range 0.8–1.0 (n=53) (Table 2).
This was carried out to assess whether there should be concern about the ratio producing false negative results, and how useful it was to actually exclude liver disease. We thus audited all patients with a borderline ratio of 0.8–1.0.
Additionally, we asked if the FIB-4 score or APRI score was used, would this have affected referral?
3. To identify the first 50 individuals in a 6-month period tested with an ALT level of 40–49 on whom the AST:ALT ratio had been cancelled (n=50) (Table 3).
Although an upper limit of 50 is taken for the normal range of ALT, there is evidence that even at levels below this a certain amount of liver inflammation is present, and, thus, different health boards use other values – such as an upper limit of normal of 40.
This last part of the project set out to identify people with a borderline abnormal ALT of 40–49, and assess whether using different scores – such as the FIB-4 or APRI scores would potentially label these individuals as having liver disease and needing to be referred
Limitations of our study
Most of these patients had a very short-term follow-up, which in many cases did not allow proper determination of their disease severity, as well as assessment of long-term mortality/morbidity risk using different scoring systems.
Secondly, we were unable to compare scores to a gold standard as in many cases a liver biopsy had not been carried out. Transient elastography and hyaluronic acid testing had been carried out in a selection of patients which allowed further characterization of fibrosis staging; however, it is appreciated that neither of these are the gold standard.
Conclusions and considerations
The AST:ALT ratio is a good test for assessing whether people should be referred to secondary care or not. This conclusion is based on the fact that many patients who were referred with a positive ratio were seen in secondary care and kept under review. However, better tests are needed to further assess their stage of disease, ideally non-invasively.
The FIB-4 (possibly in association with further tests below) may be a more sensitive/specific score to be used in diagnosing patients; however, cut-off points would need to be determined to guide the most effective use of available resources in primary and secondary care. As can be seen in the second group FIB-4 and the APRI were raised in patients which would not have been picked up by the AST:ALT ratio, which thus increases pick-up. Another consideration is, as previously mentioned, that the AST:ALT ratio tends to be raised in patients drinking excessive ethanol, even if their disease is not very advanced. Since in our cohort alcohol use was very prevalent, other scores may possibly be better suited.
The plan from now is to adopt a pathway of cascading lab tests based on patients’ alcohol consumption, BMI/metabolic syndrome markers, LFT results and automatic scoring with interpretation will be issued to GPs. Also possible is further testing – either in the community or in secondary care to further guide patients in different scoring groups – including either transient elastography (FibroScan), or further biochemical testing. NHS Lothian currently uses hyaluronic acid, and this may be a way of further classifying/evaluating people in ‘intermediate’ categories. The elastography (FibroScan) test could be another option and this is the current recommendation in the current NICE guidelines.
For any further information please feel free to contact the authors: Raphael Buttigieg: ST3 Chemical Pathology and Metabolic Medicine, NHS Greater Glasgow and Clyde, UK; raphael.buttigieg@nhs.net.
Sara Jenks: Consultant in Chemical Pathology and Metabolic Medicine, NHS Lothian, Edinburgh, UK; sjenks@nhs.net.
References
1. Gray L, Leyland AH. Alcohol. The Scottish Health Survey 2014: Volume 1: Main report (http://www.gov.scot/Publications/2015/09/6648/318753)
2. European Association for Study of Liver. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015; 63(1): 237.
3. McPherson S, Anstee QM, Henderson E, Day CP, Burt AD. Are simple noninvasive scoring systems for fibrosis reliable in patients with NAFLD and normal ALT levels? Eur J Gastroenterol Hepatol 2013; 25(6): 652–658.
4. Parkes J, Guha IN, Harris S, Rosenberg WM, Roderick PJ. Systematic review of the diagnostic performance of serum markers of liver fibrosis in alcoholic liver disease. Comp Hepatol 2012; 11(1): 5.
5. Dyson JK, Anstee QM, McPherson S. Non-alcoholic fatty liver disease: a practical approach to diagnosis and staging. Frontline gastroenterology 2014; 5(3):211–218.
The authors
Raphael Buttigieg*1 Sara Jenks2
1Department of Clinical biochemistry, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, UK
2Department of Clinical biochemistry, NHS Lothian, Royal Infirmary of Edinburgh, Edinburgh, UK
*Corresponding author
E-mail: raphael.buttigieg@nhs.net
Unlocking the full potential of digital pathology in routine diagnosis
, /in Featured Articles /by 3wmediaThe digitalization of pathology is seen as a challenging but transformative process. It is one of the fastest growing areas in healthcare and Philips Digital Pathology Solutions is a pioneer and leader in this field.
The technique creates digitalized slides from patient samples, allowing pathologists to review and share clinical data within seconds.
Digital enhances overall pathology workflow and productivity, while additionally the use of image recognition and smart software will further help pathologists to work more efficiently. It also opens up professional opportunities, globally, for remote and collaborative working. Ultimately, it has the potential to enhance patient care.
European pathology innovators like Dr Ivo van den Berghe, director of surgical pathology at the AZ Sint-Jan Bruges Hospital, Belgium, were quick to seize the initiative, understanding that digital pathology would be an enabling technology: improving patient safety, removing subjectivity and leading to new diagnostic insights.
Fundamental impact on workflow
The Bruges laboratory has a high and often challenging workload, handling around 80,000 slides a year generated by around 17,500 clinical cases. For Dr van den Berghe, the deciding factors in favour of digitalizing pathology in the clinical setting are: exceptional image quality, accuracy, measurable standardization and turnaround time. He first anticipated its impact more than eight years ago, but it was not until he began actively collaborating with Philips that his vision came to fruition. “Image, speed, quality, magnification – with Philips, they have now reached a level where we can say yes – we are there.”
The opportunity for him to instigate a complete reappraisal of the laboratory’s processes, as well as reengineering the workflow, came in 2013 when the hospital’s pathology service moved into new premises. Since then, Dr van den Berghe has worked closely with the Philips lntelliSite Pathology Solution to introduce digital pathology into routine diagnosis. “Philips could see the bigger picture from the start – and together, we have fundamentally changed the way the histopathology lab works,” he explained.
“Until now, there has been no opportunity for objectivity; pathology was therefore more of an art rather than a science. Digital pathology enables the lab to replace the subjective nature of manual slide inspection under the microscope. It enhances clinical confidence in our histopathology findings by delivering the right result first time.”
Dr van den Berghe stressed: “For our partnership to succeed, it was not simply a question of upgrading the lab’s hardware and IT. Our lab required a partner who shared our vision and would be open-minded and adaptable in understanding our changing workflow requirements. It involved matching processes and people so that they could work effectively within a modern laboratory environment.”
Philips IntelliSite Pathology Solution is an automated digital pathology image creation, management and viewing system which combines an ultra-fast scanner and image management system with dedicated software tools. The aim is to facilitate the quality of diagnosis, with the potential to allow new therapies to be developed and ultimately enhance patient care. Already available in Europe, it recently became the first global digital pathology solution marketed for primary diagnostic use in the U.S.
Confidence in results interpretation
One of the most important requirements when deciding on a digital pathology solution is image quality. Dr van den Berghe explained: “If you don’t have the highest quality available, then you can’t work effectively. For something like a polyp biopsy or with chronic gastritis, that might be acceptable. However, when working with special stains, perhaps looking for mitotic figures, identifying an infiltrate or dealing with kidney tissue samples, you need the highest quality and magnification, and you need this upfront.”
Digital pathology delivers measurable levels of standardization which enhances overall quality and confidence in results. This is particularly important with the interpretation of immunohistochemistry stains. Being able to use the accuracy of whole slide imaging to determine the degree of positivity and whether to give chemotherapy or not to a patient could be very decisive in therapy. In lymphoma pathology, for instance, where typically there are numerous stains, digital pathology enables up to 10 slides to be opened in one screen so that the histopathologist can easily align them to compare different regions in the same lymph nodes. The same image can be reviewed remotely with peers.
Alongside quality, time is a critical factor. “From the moment a biopsy is taken from a patient, the clock starts ticking,” Dr van den Berghe added: “Our task is to have a turnaround time (TAT), from the biopsy to the validated report, which is as short as possible. So, the performance of the whole slide image (WSI) scanner is of equal importance. It is no use having a high quality image if you have to wait a day to see one whole slide – that won’t work.”
Scan quality, speed and performance
Delays in scanning times can also be avoided by standardizing the workflow, and reducing the need to rescan by ensuring accuracy, so that each scan is right the first time. As part of their digital pathology review process, the lab evaluated several different scanners alongside the Philips system; and identified significant discrepancies in workflow performance which could also affect TAT. If a slide’s image scan is rejected for any reason, some scanners stop operating without manual intervention, preventing further whole slide images being created and holding up workflow.
“Our scanners run overnight, so we cannot risk leaving the department and have a problem then developing which stops the scanner and the next day we arrive to find there are no slides,” said Dr van den Berghe. “With the Philips scanner, the system simply carries on.”
If the quality of one slide is flagged up for any reason, the Ultra Fast Scanner (UFS) maintains continuous production without stopping, which helps streamline overnight operation. It does not require manual corrections or rescans that may interrupt the workflow or delay a pathologist from reviewing cases. Further, it can scan one slide of 15 x 15mm tissue) within 60 seconds including the total handling time. The ease of use of the Philips scanner also helps to streamline workflow. “Very easy to use, open door, load the slides, close the door, and start – that is what a whole slide scanner should do, while delivering the highest quality and throughput possible,” he added.
Long term storage was one of the first lab processes to benefit from digitalization. The lab stores around 200,000 slides each year and ‘increased traceability and faster access without mix-ups’ has had a huge impact on overall productivity and time management. It was also helpful in enlisting staff support as the changes were implemented. “The time-saving benefits of digitalization make work less stressful for the lab technicians and there is more balance in their job, “explained Dr van den Berghe.
“As well as relying on automated traceability for stored slides, they no longer have to spend time sorting out slides by case number and by pathologist, just put the slides into the racks and load into the scanner. Our LIS automatically assigned the slides to the appropriate or subspecialist pathologist.”
Enhances multi-disciplinary collaboration
Referring to histopathology colleagues, Dr van den Berghe believes that whole slide imaging will ‘revitalize the profession’, boosting global collaboration and enhancing their diagnostic reputation. He already sees greater collaboration in the multi-disciplinary consultation meetings within the hospital, where colleagues, wherever they are based, can simply log into the system and review all the relevant patient slides. The next step will be to expand their digital platform consultancy so that the lab can add as many hospitals as possible into their consulting network, both national and international.
“I anticipate that the use of digital pathology in difficult and diagnostically rare diseases will lead to centralization of expertise through our consultancy platform, enhancing expert diagnosis. And this, at the end, will lead to the best patient care,” he confirmed.
When slides are digitized, he says, true collaboration is possible. “This is the power of digital slides. We can manage workflow and streamline everything in terms of image management, image sharing, and image analysis—simply not possible with the microscope and glass slides.”
Dr van den Berghe believes that digitalizing pathology and the resulting standardization of results will lead to more consistent, overall quality. For example, a lab can set its own parameter for an acceptable quality threshold and create a specific rule for image quality. “Any image that does not meet that predetermined measure will then automatically fail.”
New parameters for quality
However, he accepts that setting new objective parameters for quality control will have an impact on existing lab protocols, especially where decision making is still subjective. He draws specific attention to Hematoxylin & Eosin (H&E) slides and the use of their colour containers. In most labs, these are still used until someone subjectively decides that quality has started to decline. “Requiring good quality here is paramount to being confident in the results and making a positive contribution to improving outcomes. With digital pathology we need to actively discourage this subjective process,” stressed Dr van den Berghe.
Pathology plays a pivotal role in the diagnosis of disease, as well as determining and monitoring treatment. However, the need to master the manual technique of the microscope has increasingly been seen as old-fashioned and many believe it deters the next generation of recruits into the profession. “Digitizing pathology will end their reluctance,” predicts Dr van den Berghe.
When he started his journey eight years ago, he recognized that whole slide imaging could only reach its potential as part of a fully digitalized pathology workflow. Philips has created such a solution with IntelliSite. The company predicts that the digitalization of pathology will open up the sharing of clinical information with pathologists in the lab or working remotely, helping to build global networks of expertise. While their solution helps the lab satisfy demand for increased productivity, it is ultimately the patient who benefits – with faster diagnosis and enhanced outcomes.
The author
Ivo van den Berghe, MD,
Director of surgical pathology
AZ Sint-Jan Bruges Hospital, Belgium
MiniCollect® Capillary Blood Collection System
, /in Featured Articles /by 3wmediaElisa, Antibodies & Instruments
, /in Featured Articles /by 3wmediaRESIST – Get precise identification of your CPO
, /in Featured Articles /by 3wmediaCystatin C FS – The New IT Gold Standard
, /in Featured Articles /by 3wmedia