Advances in cellular pathology techniques will improve diagnostic medicine. However, such improvements have to overcome many challenges including variations in pre-analytical sample processing, bioinformatics data analysis and clinical interpretation of data. In order to resolve such challenges, bioinformatics needs to become more tightly coupled to the experimental methodology development.
by Dr Rifat Hamoudi, Dr Joshua Kapp, Sevgi Umur and Michael Gandy
Introduction
Molecular diagnostics within cellular pathology have been performed since the late 1990s and have developed to include a range of techniques including short tandem repeat (STR) identity analysis, classification of tumours and clonality determinations in hematopathology. More recently, with the introduction of qPCR and more recently of next generation sequencing (NGS) as shown in Figure 1, precision medicine testing for targeted therapies has rapidly gained access to daily practice and become a challenge for molecular biologists and pathologists to provide the most accurate and relevant information. As part of this testing process we discuss two major challenges which have developed, these are:
- Firstly, pre-analytical processing of formalin-fixed paraffin-embedded (FFPE) tissue, has shown to be a critical determinant in the accuracy of downstream molecular testing in specialities such as mutational screening for targeted therapies.
- Secondly, bioinformatics has become a bottleneck in data processing and interpretation, with the processing, analysis and reporting of the data shown variability between different laboratories.
This article looks to raise the awareness of these issues and presents possible areas for consideration to aid in their resolution.
Variation in pre-analytical sample processing of FFPE samples may lead to discrepancies in mutational testing of actionable genes
Within cellular pathology, the majority of molecular diagnostic clinical sample testing is now carried out on FFPE samples. Generally the tissue is screened using hematoxylin and eosin stained sections to estimate the tumour content before the preparation process of material for subsequent molecular testing, as shown in Figure 2.
Recent studies have shown that variations in pre-analytical processing of samples lead to discrepancies in downstream molecular diagnostic testing [1–3]. The variations using singleplex mutational screening were largely due to the DNA extraction system used [2, 3], quantitation using spectrophotometry and training of laboratory staff as one study showed that pre-analytical variation was significant even among experienced laboratories [3]. In addition both DNA quantitation and integrity measurements play important roles in the accuracy of downstream multiplex testing using NGS.
In order to resolve some of those issues it is important to include control series of diagnostic samples, prepared according to the diagnostic operating procedures of the laboratory with a variety of known mutations comprising missense mutations, simple and complex deletions and insertions. Assay control using known representative DNA samples from the FFPE tissue is also essential to ensure that the process of DNA extraction, quantitation and integrity measurements are performed correctly and consistently. This is important as DNA quality has a major effect on NGS performance, i.e. poor quality DNA causes a higher error rate [3].
In addition, differences in quantitation measurements need to be accounted for, since the different instruments used have different ways of measuring the concentration of DNA. For example, variations can be seen between systems such as Nanodrop spectrophotometry and Qubit fluorometry. Measurement of DNA integrity is also important and most labs use assays such as BIOMED [4, 5] or qPCR as the ‘gold standard’ measure.
Also European external quality assurance (EQA) programmes for mutation detection of solid tumours such as European Society for Pathology (ESP, www.esp-pathology.org), European Molecular Genetics Quality Network (EMQN, www.emqn.org), and United Kingdom National External Quality Assessment Scheme UK NEQAS for Molecular Pathology (www.ukneqas.org.uk and www.ukneqas-molgen.org.uk) may consider including pre-analytical (e.g. pre-PCR) component in their assessment for mutation detection from FFPE samples.
Discrepancies in variant-calling pipelines and high-throughput sequencing clinical interpretation
Most diseases such as cancer and inherited diseases are driven by genomic alterations. Recent advances in high-throughput sequencing technologies have enabled the identification of somatic mutations at very high resolution. However, accurate somatic mutation-calling using high-throughput sequence data remains one of the major challenges in genomics. For somatic mutation-calling, one looks for a site in which a variant allele exists in the tumour sample but not in the normal sample. Even with the sequence data from a normal sample, variant-calling in high-throughput sequencing data is challenging due to the multiple potential sources of errors. For example, artefacts occurring during PCR amplification or targeted capture (e.g. exome-capture), machine sequencing errors, and incorrect local alignments of reads are all well documented sources of error [6–8]. Tumour heterogeneity and normal contamination contribute additional challenges for the tumour samples [9].
Various studies have shown low concordance between different variant callers and bioinformatics analysis pipelines. Wang et al. [10] compared six variant callers on whole exome sequencing melanoma sample and matched blood of 18 lung tumour–normal pairs and seven lung cancer cell lines carried out on the Illumina HiSeq 2000. The results showed discordance between the six variant callers, and the top two performing callers could only detect 86% and 71% of validated mutations respectively. O’Rawe et al. [11] compared the analysis of five different Illumina alignment and variant-calling pipelines on 15 exome sequencing data carried out using Illumina HiSeq 2000 and Agilent SureSelect version 2 capture kit at 120X mean coverage. Results showed variant-calling concordance of 57.4% between the five different Illumina pipelines across all 15 exomes with the authors urging more caution when analysing individual genomes in genomic medicine. In addition, comparison of the two most prominent cancer genome sequencing databases; catalogue of somatic mutations in cancer (COSMIC) [12] and Cancer Cell Line Encyclopaedia (CCLE) [13] revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.4% conformity), where the main reason for such discrepancy is inadequate sequencing of GC-rich areas of the exome [14].
In addition to the above, various studies have shown discrepancies in the interpretation of genomic data between the clinician and diagnostic laboratory. Shashi et al. [15] tried to follow up the results of 93 patients who underwent exome sequencing. They investigated how the clinical interpretation of the lab results changed the diagnosis and its conformity with it. Overall, the results showed that in 25% of patients (24/93), exome sequencing showed a positive result and in 80% (19/24) of cases, the clinicians agreed with the molecular diagnosis of the lab. However, in 20% of patients reported to be positive by the diagnostic lab, the clinicians thought that the suggested molecular diagnosis was not correct. In addition, 5% of patients that were considered negative by the exome lab or had a lower confidence diagnosis, were eventually found to be positive when the exome data was reviewed by clinicians. In summary the results showed 20% false positives and 5% false negatives when comparing the interpretation of genomic data between different healthcare staff.
However, it is worth noting that all the above studies used samples with high molecular weight DNA from cell lines, fresh frozen tissue or blood and carrying out the same studies above using FFPE samples has the potential to lead to further discrepancies due to the degraded DNA inherent to those samples increases the variation at the pre-analytical steps resulting in downstream discrepancies in mutational profiling. This crates it a big challenge in the development of bioinformatics pipelines required to produce consistent clinically reliable data.
One way to resolve some of the bioinformatics related issues is to exchange the raw datasets between laboratories that preferentially use different software as part of the software validation process to establish the ability of the various laboratories to detect identical gene mutations. In addition, new software updates need to be validated by analysis of prior NGS datasets covering simple and complex mutations. Finally, raw NGS datasets need to be included in EQA programmes as in silico assessment.
Conclusion
Although the above discussion very briefly surveys the current landscape in cellular pathology, the future of molecular diagnostics will undoubtedly develop to include integrated RNA expression analysis, DNA amplification and epigenetics. Each methodology will have its own idiosyncrasies and will require the development of new clinically validated bioinformatics pipeline. Additionally, the need for a novel bioinformatics system to support integrative analysis will become essential. Although previously attempted [16], new systems need to be developed to support integrative high-throughput sequencing analysis.
However, before novel bioinformatics software solutions can be devised for big data, concerns about bioinformatics software development need to be addressed. A potential starting point to address this is via supporting new bioinformatics courses that use software engineering, computer programming and mathematical modelling of biological complexity at their core, supporting the education of future bioinformaticians in the art of bioinformatics software development. This will help support a change in the current paradigm where much of the current bespoke bioinformatics software today has been developed by local institutions in relative isolation, often in conjunction within the framework of a specialist area experimental research program [17].
The future landscape highly likely see the validation of wet chemistries (laboratory and clinical based) and dry (computational based) experiments carried out in more tightly coupled format than is currently performed, supporting clinical product development in the commercial market. Also, the future will see more focus on the development of more efficient adaptive algorithms that address the clinical questions, leading to faster analysis and improving the clarity in the interpretation of the data.
In conclusion, within cellular pathology the incremental development of pre-analytical processing from FFPE samples coupled with more efficient adaptive bioinformatics algorithms implementation are key areas of focus and crucial to the further advancement of next generation molecular pathology.
References
1. Carrick DM, Mehaffey MG, Sachs MC, Altekruse S, et al. Robustness of Next Generation Sequencing on older formalin-fixed paraffin-embedded tissue. PLoS One 2015; 10: e0127353.
2. Heydt C, Fassunke J, Kunstlinger H, Ihle MA, et al. Comparison of pre-analytical FFPE sample preparation methods and their impact on massively parallel sequencing in routine diagnostics. PLoS One 2014; 9: e104566.
3. Kapp JR, Diss T, Spicer J, Gandy M, et al. Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial. J Clin Pathol. 2015; 68: 111–118.
4. Johnson NA, Hamoudi RA, Ichimura K, Liu L, et al. Application of array CGH on archival formalin-fixed paraffin-embedded tissues including small numbers of microdissected cells. Lab Invest. 2006; 86: 968–978.
5. van Dongen JJ, Langerak AW, Bruggemann M, Evans PA, et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98–3936. Leukemia 2003; 17: 2257–2317.
6. Meacham F, Boffelli D, Dhahbi J, Martin DI, et al. Identification and correction of systematic error in high-throughput sequence data. BMC Bioinformatics 2011; 12: 451.
7. Nakamura K, Oshima T, Morimoto T, Ikeda S, et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 2011; 39: e90.
8. Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011; 12: 443–451.
9. Gerlinger M, Rowan AJ, Horswell S, Larkin J, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012; 366: 883–892.
10. Wang Q, Jia P, Li F, Chen H, et al. Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med. 2013; 5: 91.
11. O’Rawe J, Jiang T, Sun G, Wu Y, et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 2013; 5: 28.
12. Forbes SA, Beare D, Gunasekaran P, Leung K, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015; 43: D805-D811.
13. Barretina J, Caponigro G, Stransky N, Venkatesan K, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483: 603–607.
14. Hudson AM, Yates T, Li Y, Trotter EW, et al. Discrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery. Cancer Res. 2014; 74: 6390–6396.
15. Shashi V, McConkie-Rosell A, Schoch K, Kasturi V, et al. Practical considerations in the clinical application of whole-exome sequencing. Clin Genet. 2015; doi: 10.1111/cge.12569.
16. Watkins AJ, Hamoudi RA, Zeng N, Yan Q, et al. An integrated genomic and expression analysis of 7q deletion in splenic marginal zone lymphoma. PLoS One 2012; 7: e44997.
17. Prins P, de Ligt J, Tarasov A, Jansen RC, et al. Toward effective software solutions for big biology. Nat Biotechnol. 2015; 33: 686–687.
The authors
Rifat Hamoudi*1 PhD, Joshua Kapp1 MBBS, Sevgi Umur2 BSc and Michael Gandy3 MSc
1Division of Surgery and Interventional Science, University College London, London, UK
2Genonymous Sciences, Küçükbakkalköy, Defne Sokak, Flora Residence Istanbul,Turkey
3Health Services Laboratories, 60 Whitfield Street, London, UK
*Corresponding author
E-mail: r.hamoudi@ucl.ac.uk
Variations in pre-analytical FFPE sample processing and bioinformatics: challenges for next generation molecular diagnostic testing in clinical pathology
, /in Featured Articles /by 3wmediaAdvances in cellular pathology techniques will improve diagnostic medicine. However, such improvements have to overcome many challenges including variations in pre-analytical sample processing, bioinformatics data analysis and clinical interpretation of data. In order to resolve such challenges, bioinformatics needs to become more tightly coupled to the experimental methodology development.
by Dr Rifat Hamoudi, Dr Joshua Kapp, Sevgi Umur and Michael Gandy
Introduction
Molecular diagnostics within cellular pathology have been performed since the late 1990s and have developed to include a range of techniques including short tandem repeat (STR) identity analysis, classification of tumours and clonality determinations in hematopathology. More recently, with the introduction of qPCR and more recently of next generation sequencing (NGS) as shown in Figure 1, precision medicine testing for targeted therapies has rapidly gained access to daily practice and become a challenge for molecular biologists and pathologists to provide the most accurate and relevant information. As part of this testing process we discuss two major challenges which have developed, these are:
This article looks to raise the awareness of these issues and presents possible areas for consideration to aid in their resolution.
Variation in pre-analytical sample processing of FFPE samples may lead to discrepancies in mutational testing of actionable genes
Within cellular pathology, the majority of molecular diagnostic clinical sample testing is now carried out on FFPE samples. Generally the tissue is screened using hematoxylin and eosin stained sections to estimate the tumour content before the preparation process of material for subsequent molecular testing, as shown in Figure 2.
Recent studies have shown that variations in pre-analytical processing of samples lead to discrepancies in downstream molecular diagnostic testing [1–3]. The variations using singleplex mutational screening were largely due to the DNA extraction system used [2, 3], quantitation using spectrophotometry and training of laboratory staff as one study showed that pre-analytical variation was significant even among experienced laboratories [3]. In addition both DNA quantitation and integrity measurements play important roles in the accuracy of downstream multiplex testing using NGS.
In order to resolve some of those issues it is important to include control series of diagnostic samples, prepared according to the diagnostic operating procedures of the laboratory with a variety of known mutations comprising missense mutations, simple and complex deletions and insertions. Assay control using known representative DNA samples from the FFPE tissue is also essential to ensure that the process of DNA extraction, quantitation and integrity measurements are performed correctly and consistently. This is important as DNA quality has a major effect on NGS performance, i.e. poor quality DNA causes a higher error rate [3].
In addition, differences in quantitation measurements need to be accounted for, since the different instruments used have different ways of measuring the concentration of DNA. For example, variations can be seen between systems such as Nanodrop spectrophotometry and Qubit fluorometry. Measurement of DNA integrity is also important and most labs use assays such as BIOMED [4, 5] or qPCR as the ‘gold standard’ measure.
Also European external quality assurance (EQA) programmes for mutation detection of solid tumours such as European Society for Pathology (ESP, www.esp-pathology.org), European Molecular Genetics Quality Network (EMQN, www.emqn.org), and United Kingdom National External Quality Assessment Scheme UK NEQAS for Molecular Pathology (www.ukneqas.org.uk and www.ukneqas-molgen.org.uk) may consider including pre-analytical (e.g. pre-PCR) component in their assessment for mutation detection from FFPE samples.
Discrepancies in variant-calling pipelines and high-throughput sequencing clinical interpretation
Most diseases such as cancer and inherited diseases are driven by genomic alterations. Recent advances in high-throughput sequencing technologies have enabled the identification of somatic mutations at very high resolution. However, accurate somatic mutation-calling using high-throughput sequence data remains one of the major challenges in genomics. For somatic mutation-calling, one looks for a site in which a variant allele exists in the tumour sample but not in the normal sample. Even with the sequence data from a normal sample, variant-calling in high-throughput sequencing data is challenging due to the multiple potential sources of errors. For example, artefacts occurring during PCR amplification or targeted capture (e.g. exome-capture), machine sequencing errors, and incorrect local alignments of reads are all well documented sources of error [6–8]. Tumour heterogeneity and normal contamination contribute additional challenges for the tumour samples [9].
Various studies have shown low concordance between different variant callers and bioinformatics analysis pipelines. Wang et al. [10] compared six variant callers on whole exome sequencing melanoma sample and matched blood of 18 lung tumour–normal pairs and seven lung cancer cell lines carried out on the Illumina HiSeq 2000. The results showed discordance between the six variant callers, and the top two performing callers could only detect 86% and 71% of validated mutations respectively. O’Rawe et al. [11] compared the analysis of five different Illumina alignment and variant-calling pipelines on 15 exome sequencing data carried out using Illumina HiSeq 2000 and Agilent SureSelect version 2 capture kit at 120X mean coverage. Results showed variant-calling concordance of 57.4% between the five different Illumina pipelines across all 15 exomes with the authors urging more caution when analysing individual genomes in genomic medicine. In addition, comparison of the two most prominent cancer genome sequencing databases; catalogue of somatic mutations in cancer (COSMIC) [12] and Cancer Cell Line Encyclopaedia (CCLE) [13] revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.4% conformity), where the main reason for such discrepancy is inadequate sequencing of GC-rich areas of the exome [14].
In addition to the above, various studies have shown discrepancies in the interpretation of genomic data between the clinician and diagnostic laboratory. Shashi et al. [15] tried to follow up the results of 93 patients who underwent exome sequencing. They investigated how the clinical interpretation of the lab results changed the diagnosis and its conformity with it. Overall, the results showed that in 25% of patients (24/93), exome sequencing showed a positive result and in 80% (19/24) of cases, the clinicians agreed with the molecular diagnosis of the lab. However, in 20% of patients reported to be positive by the diagnostic lab, the clinicians thought that the suggested molecular diagnosis was not correct. In addition, 5% of patients that were considered negative by the exome lab or had a lower confidence diagnosis, were eventually found to be positive when the exome data was reviewed by clinicians. In summary the results showed 20% false positives and 5% false negatives when comparing the interpretation of genomic data between different healthcare staff.
However, it is worth noting that all the above studies used samples with high molecular weight DNA from cell lines, fresh frozen tissue or blood and carrying out the same studies above using FFPE samples has the potential to lead to further discrepancies due to the degraded DNA inherent to those samples increases the variation at the pre-analytical steps resulting in downstream discrepancies in mutational profiling. This crates it a big challenge in the development of bioinformatics pipelines required to produce consistent clinically reliable data.
One way to resolve some of the bioinformatics related issues is to exchange the raw datasets between laboratories that preferentially use different software as part of the software validation process to establish the ability of the various laboratories to detect identical gene mutations. In addition, new software updates need to be validated by analysis of prior NGS datasets covering simple and complex mutations. Finally, raw NGS datasets need to be included in EQA programmes as in silico assessment.
Conclusion
Although the above discussion very briefly surveys the current landscape in cellular pathology, the future of molecular diagnostics will undoubtedly develop to include integrated RNA expression analysis, DNA amplification and epigenetics. Each methodology will have its own idiosyncrasies and will require the development of new clinically validated bioinformatics pipeline. Additionally, the need for a novel bioinformatics system to support integrative analysis will become essential. Although previously attempted [16], new systems need to be developed to support integrative high-throughput sequencing analysis.
However, before novel bioinformatics software solutions can be devised for big data, concerns about bioinformatics software development need to be addressed. A potential starting point to address this is via supporting new bioinformatics courses that use software engineering, computer programming and mathematical modelling of biological complexity at their core, supporting the education of future bioinformaticians in the art of bioinformatics software development. This will help support a change in the current paradigm where much of the current bespoke bioinformatics software today has been developed by local institutions in relative isolation, often in conjunction within the framework of a specialist area experimental research program [17].
The future landscape highly likely see the validation of wet chemistries (laboratory and clinical based) and dry (computational based) experiments carried out in more tightly coupled format than is currently performed, supporting clinical product development in the commercial market. Also, the future will see more focus on the development of more efficient adaptive algorithms that address the clinical questions, leading to faster analysis and improving the clarity in the interpretation of the data.
In conclusion, within cellular pathology the incremental development of pre-analytical processing from FFPE samples coupled with more efficient adaptive bioinformatics algorithms implementation are key areas of focus and crucial to the further advancement of next generation molecular pathology.
References
1. Carrick DM, Mehaffey MG, Sachs MC, Altekruse S, et al. Robustness of Next Generation Sequencing on older formalin-fixed paraffin-embedded tissue. PLoS One 2015; 10: e0127353.
2. Heydt C, Fassunke J, Kunstlinger H, Ihle MA, et al. Comparison of pre-analytical FFPE sample preparation methods and their impact on massively parallel sequencing in routine diagnostics. PLoS One 2014; 9: e104566.
3. Kapp JR, Diss T, Spicer J, Gandy M, et al. Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial. J Clin Pathol. 2015; 68: 111–118.
4. Johnson NA, Hamoudi RA, Ichimura K, Liu L, et al. Application of array CGH on archival formalin-fixed paraffin-embedded tissues including small numbers of microdissected cells. Lab Invest. 2006; 86: 968–978.
5. van Dongen JJ, Langerak AW, Bruggemann M, Evans PA, et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98–3936. Leukemia 2003; 17: 2257–2317.
6. Meacham F, Boffelli D, Dhahbi J, Martin DI, et al. Identification and correction of systematic error in high-throughput sequence data. BMC Bioinformatics 2011; 12: 451.
7. Nakamura K, Oshima T, Morimoto T, Ikeda S, et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 2011; 39: e90.
8. Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011; 12: 443–451.
9. Gerlinger M, Rowan AJ, Horswell S, Larkin J, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012; 366: 883–892.
10. Wang Q, Jia P, Li F, Chen H, et al. Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med. 2013; 5: 91.
11. O’Rawe J, Jiang T, Sun G, Wu Y, et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 2013; 5: 28.
12. Forbes SA, Beare D, Gunasekaran P, Leung K, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015; 43: D805-D811.
13. Barretina J, Caponigro G, Stransky N, Venkatesan K, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483: 603–607.
14. Hudson AM, Yates T, Li Y, Trotter EW, et al. Discrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery. Cancer Res. 2014; 74: 6390–6396.
15. Shashi V, McConkie-Rosell A, Schoch K, Kasturi V, et al. Practical considerations in the clinical application of whole-exome sequencing. Clin Genet. 2015; doi: 10.1111/cge.12569.
16. Watkins AJ, Hamoudi RA, Zeng N, Yan Q, et al. An integrated genomic and expression analysis of 7q deletion in splenic marginal zone lymphoma. PLoS One 2012; 7: e44997.
17. Prins P, de Ligt J, Tarasov A, Jansen RC, et al. Toward effective software solutions for big biology. Nat Biotechnol. 2015; 33: 686–687.
The authors
Rifat Hamoudi*1 PhD, Joshua Kapp1 MBBS, Sevgi Umur2 BSc and Michael Gandy3 MSc
1Division of Surgery and Interventional Science, University College London, London, UK
2Genonymous Sciences, Küçükbakkalköy, Defne Sokak, Flora Residence Istanbul,Turkey
3Health Services Laboratories, 60 Whitfield Street, London, UK
*Corresponding author
E-mail: r.hamoudi@ucl.ac.uk
Digital pathology – late starter, becoming indispensable
, /in Featured Articles /by 3wmediaDigital pathology is a computer-based imaging environment which enables the analysis and storage/retrieval of information from a digital slide. It is considered to be one of the most promising recent developments in diagnostic medicine, with the potential to provide better, quicker and more cost-effective diagnosis and prognosis, especially for managing diseases like cancers.
Digital pathology is closely associated with the term ‘virtual’ microscopy, since images are viewed remotely, without a microscope or slides, after being transferred over a hospital network or the Internet.
Whole slide imaging
A key technology driver of digital pathology is whole slide imaging (WSI), sometimes also described as whole slide digital imaging. WSI scans and converts specimen glass slides into digital images, which are made accessible by software for display on a computer monitor. Digitized slides allow analysis via computer algorithms, which automate the counting of structures and quantitatively classify tissue condition. This task is otherwise performed, painstakingly, by pathologists using a microscope to view, analyse and stain tissue slides.
The pace of development in virtual microscopy has accelerated. Recent advances in scanning technology allow for achieving over 100,000 dpi resolutions, in other words approaching the level of optical microscopes.
Cancer staging versus grading: the role of pathology
Digital pathology offers particular promise in the grading of tumours. Tumour ‘grade’ is different from the ‘stage’ of a cancer.
Cancer stage refers to the size of a tumour and whether or not cancer cells have spread in the body.
Pathologists play a role in providing staging information, alongside physical examination, imaging and lab tests. Physicians select different combinations of each of these modalities for staging.
By contrast, grading is principally a pathologist’s area of expertise.
Grading scales
The grade describes a tumour and the likelihood of its growth and spread, based on the abnormality of tumour cells as seen under a microscope.
Tumours are typically graded on a 1-4 scale. A lower grade indicates better prognosis, while higher grades tend to grow and spread more quickly, thus requiring more aggressive treatment. Grade 1 tumours are usually described as “well-differentiated” with tumour cells and tissue appearing close to normal. Grade 3 and 4 tumours look least like normal cells and tissue. They are often described as “poorly differentiated” or “undifferentiated,” and tend to grow and spread faster than tumours with a lower grade.
The pathologist and visual perspectives
One of the strongest arguments in favour of digital pathology is that microscopes require pathologists to always possess keen eyesight to determine the ‘differentiation’ required for assigning a grade to a tumour. Like many other healthcare professionals, pathologists are also burdened by heavy workloads. This, in turn, can impact on visual acuity and interpretation.
Digitized images, in contrast, can quantify the differentiation (and grading) process via algorithms, reduce the risk of human error and improve accuracy.
Glass slides and inconsistent interpretation
The challenge of consistency in pathology has been a vexing issue for some time. In March 2015, the ‘Journal of the American Medical Association’ (JAMA) published the results of a study to “quantify the magnitude of diagnostic disagreement among pathologists.” The study focused on pathologists interpreting breast biopsies from glass slides in eight US states and noted that although a breast pathology diagnosis provided “the basis for clinical treatment and management decisions,” its accuracy was “inadequately understood.” Among its findings – one in four cases did not show consonance of individual pathologists’ interpretations with expert consensus.
Disagreement with the reference diagnosis was statistically significant among pathologists who interpreted lower weekly case volumes or worked in smaller practices – confirming the observation about the inverse correlation between workload on the one side, and quality of eyesight on the other.
Digital pathology and second opinions
To address such variations in diagnoses, second opinions have become commonplace. However, for glass slides, a second opinion entails long lead times and complexities in a pathologist’s workflow (from glass packaging and transport, and at the other end, unpacking materials, verifying sample/reference case, registering the case in a laboratory information system etc.). Many pathologists are forced to cope quietly with a difficult decision – weighing up the value of a second opinion against the extra waiting time for a patient.
Digital pathology streamlines access to second opinions, enabling quicker and more accurate delivery of diagnoses. Both these correlate strongly to successful treatment outcomes. Telemedicine has taken explicit note about this potential. In 2014, the American Telemedicine Association published draft guidelines on the use of digital pathology in telemedicine.
Radiology and pathology: collaborative cousins
Pathology is involved in almost all cancer diagnoses.
Digital pathology is being seen as both a catalyst and enabler for more collaboration across specialties, beginning with radiology – one of the first fields to be digitized.
A September 2012 ‘BMC Medicine’ article titled ‘Integrating Pathology and Radiology Disciplines: An Emerging Opportunity?’ argues for an end to traditional pathology-radiology workflows where the two specialties “form the core of cancer diagnosis” but remain “ad hoc and occur in separate ‘silos’, even though “the opportunity for pathology-radiology integration to improve patient care is great, and more importantly, the tools to achieve this exist.”
DICOM and HIPAA
Until recently, digital pathology was hampered by a lack of standards for storing and transferring images, among other things, to be more in line with modern PACS systems storing radiology images. However, this was successfully addressed by DICOM (Digital Imaging and Communications in Medicine) supplement for digital pathology (No. 145), which was released in July 2010.
According to a May 2011 report in the ‘Journal of Pathology Informatics’, the DICOM supplement standard was hailed by “everyone involved in the field of digital pathology” since it made it easier for hospitals “to integrate digital pathology into their already established systems without adding too much overhead costs.”
Besides, it was seen to enable different vendors developing scanners “to upgrade their products to storage systems that are common across all systems.”
There is already sufficient integration between digital pathology systems (DPS) and anatomic pathology laboratory information systems (APLIS) to provide pathologists with access to images and image analysis data from either, and input it to a Patient Report. On the regulatory front, DPS vendors are also well placed to support HIPAA compliance by encrypting protected health information (PHI) metadata such as slide labels, hospital, patient and specimen information, etc.
A March 2007 issue of ‘Neuroimage’ points to another major attribute of digital pathology, namely the capacity for data mining.”
Digital pathology in medical education
So far, the key application of digital pathology has been in teaching. As the University of Minnesota observes, “virtual microscopes can transform traditional teaching methods by removing the reliance on physical space, equipment, and specimens to a model that is solely dependent upon computer-internet access. This rich database is enhanced with patient clinical presentations, laboratory data, comprehensive slide interpretations, and diagnoses.”
Also in the US, a partnership between Oklahoma University Medical Center (OUMC), the Children’s Hospital, and the University of Oklahoma College of Medicine, observes that digital pathology promotes efficiency and cost-effectiveness as a teaching tool as well as in using digital slides for consultation with patients referred to OUMC from other hospitals.
Barriers to digital pathology
Barriers to the more widespread implementation of digital pathology have also been recently assessed. These concern economics (mainly return on investment) and consistency and methodological robustness in WSI.
ROI
Unlike digital radiology which has a longer legacy and a stronger case for ROI (return on investment) – principally in terms of replacing film, the arguments for digital pathology are less obvious.
A study by a Swedish hospital found the following justifications for digital pathology: savings of time in administrative tasks (13%), slide review (6%) and supervision (3.1%), alongside an increase in efficiency of administrative tasks (100%), supervision (33%) and slide review (16%).
In terms of productivity per pathologist, the gain attained by digital pathology was 10%, while overall time savings were 24%.
Consistency in digital pathology interpretations
The second challenge facing digital pathology has been to determine the difference of interpretation of whole-slide images from glass-slide interpretation in difficult surgical cases, and the impact of such differences. This issue has been the subject of a study, with an article on the findings published in the December 2009 issue of the ‘Archives of Pathology & Laboratory Medicine’.
Overall concordance between digital whole-slide and standard glass-slide interpretations was 91%, with agreement among digital, glass, and reference diagnoses in 85% of cases. 9% of digital cases were discordant with both reference and glass diagnoses. This was due to incorrect digital whole-slide interpretation, mainly because of issues such as fine resolution and navigating ability at high magnification.
FDA approval
One of the biggest obstructions to the growth of digital pathology has been the absence of approval by the US Food and Drug Administration (FDA) for primary diagnosis. Several EU countries allow pathologists to use WSI for primary diagnosis, with some flexibility. For instance, in Sweden, slides are digitally scanned but also physically delivered to a consulting pathologist who has the choice to review the slides on screen, in the microscope, or both.
However, much of the technology development in digital pathology – as well as vendor interest – has originated in the US, and it is evident that freeing up digital pathology for primary diagnosis in that country would galvanize use worldwide.
US manufacturers have so far been able to market digital pathology technology for Research Use Only (RUO). Several vendors have also received one or more FDA 510 (k) clearances, with a key justification being manual and/or quantitative analysis of immunohistochemistry and/or in situ hybridization.
More developments are in the pipeline.
In February 2015, the FDA issued draft guidance for the technical performance assessment of digital pathology WSI devices. This followed an FDA Hematology and Pathology Devices Panel meeting six years previously to obtain industry feedback on replacing glass slides and conventional microscopy with whole slide images (WSI) for the purpose of rendering surgical pathology diagnosis.
On its part, the Digital Pathology Association (DPA) expects the draft guidance to lead to follow-on guidance and clarify the FDA’s expectations for WSI regulatory submissions, enabling increased access and adoption of digital pathology for clinical use.
Mass spectrometric immunoassay for top-down protein analysis
, /in Featured Articles /by 3wmediaMass spectrometry-based methods hold great promise for addressing protein heterogeneity. As a result of post-translational processing, proteins can exist in vivo as multiple proteoforms. The added information contained in the protein profile can be important in physiological and pathological states. Presented here is an overview of a mass spectrometric immunoassay (MSIA) for quantitative determination of the chemokine RANTES proteoforms. MSIA offers protein quantification and profiling in a high-throughput and time-efficient manner. Across a cohort of ~300 human plasma samples, a total of 11 different RANTES proteoforms were quantified in less than 3 hours.
by Dr O. Trenchevska, N. D. Sherma, Dr P. D. Reaven, Dr R. W. Nelson and Dr D. Nedelkov
The role of mass spectrometry in protein analyses
Mass spectrometry (MS) has proven successful in the clinical laboratory for the analysis of small molecules, but is on the rise as an emerging methodology for peptides and proteins [1]. Currently, a handful of MS-based protein assays have been adapted in the routine clinical analyses and used for in vitro diagnostic (IVD) testing [2, 3]. MS-based methodologies are the assays of choice because they can overcome the limitations of immunoassays (i.e. nonspecific binding, cross-reactivity of analytes, etc.). In order to be clinically applicable, all MS-based assays should comply with the well-established ‘fit-for-purpose’ approach and be fully validated and characterized [4]. Also, working protocols must be practical (in terms of sample preparation), as well as cost efficient, so they are price-competitive with current immunoassays. Although overcoming these requirements is still a challenge, one inevitable advantage that makes MS-based protein assays indispensable, is their unique ability to address protein heterogeneity.
The majority of clinically adapted MS-based methodologies for protein profiling are the single/multiple reaction monitoring liquid chromatography MS (SRM/MRM LC-MS) assays [5, 6] and mass spectrometric immunoassays (MSIA) [7, 8]. MRM assays are ‘bottom-up’ assays and use isotopically labelled peptides as internal reference standards for surrogate protein quantification via chosen, enzymatically generated peptides. Because SRM/MRM LC-MS assays detect only specific peptides, important information about novel proteoforms or post-translational modifications with potential clinical implications can be overlooked. MSIAs, on the other hand, follow a ‘top-down’ approach, having intact proteins as primary targets. As a result of the immunoaffinity capture of a targeted protein(s), and the ‘soft’ ionization in MALDI-TOF (matrix-assisted laser desorption/ionization–time of flight) MS, MSIA enable for detection of post-translationally modified proteoforms as well as other changes in protein structure without the harsh enzyme digestion. Literature data show that post-translationally modified proteins have the potential to be used as biomarkers [9]. Having that in mind, the proteoform detection adds a whole new dimension to the way we look at proteins.
Mass spectrometric immuno-assay for analysis of RANTES proteoforms
Here we review a mass spectrometric immunoassay (MSIA) for quantification of the chemokine RANTES proteoforms in human plasma samples. RANTES (Regulated on Activation, Normal, T-cell Expressed and Secreted), is a member of the CC chemokine family (hence its alternative name – CCL5) and is essential in the initiation and maintenance of inflammation [10]. RANTES has been studied extensively in clinical context, in association with autoimmune diseases, arthritis, diabetes, obesity and metabolic syndrome, some types of cancer and viral infections [11–13]. In addition, RANTES proteoforms have been associated with atherosclerosis and cardiovascular diseases [14].
There are several types of commercially available, as well as in-house developed immunoassays for total RANTES quantification [15]. These assays, however, are not tailored for detecting and quantifying the numerous proteoforms associated with RANTES. In previous work, we have addressed RANTES heterogeneity by qualitative and quantitative MSIA [16, 17]. In developing the quantitative MSIA for RANTES, we took on the approach of using RANTES standard and a homologous RANTES derivative – met-RANTES as an internal reference standard (IRS) for quantification. Met-RANTES is a recombinant derivative of RANTES (therefore not found in humans) and has a molecular weight (MW) of 7979.2 Da, which is in close proximity to that of full-length human RANTES (MW=7847.9 Da). Another advantage of using the RANTES/met-RANTES pair was the ability of a single anti-RANTES antibody to capture both proteins from the biological samples.
The immobilization of the anti-RANTES antibody was onto activated surfaces of affinity pipettes as previously described [17]. The quantity of the anti-RANTES antibody (7.5 µg Ab/tip) was optimized to be enough that variable RANTES concentrations in the samples could be truly quantified with the assay. Due to low plasma RANTES physiological concentration (in the ng/mL level), undiluted plasma was used for the analyses. In the analytical samples, met-RANTES was spiked at a constant concentration (V=250 µL at c=50 ng/mL), in order to produce a constant signal in the mass spectra. Following sample preparation and affinity pipette derivatization, the antibody-coated pipettes were mounted onto the head of an automated 96-channel pipettor and initially rinsed with PBS/0.1% Tween buffer. Next, the pipettes were immersed into a microplate containing the analytical samples and 500 aspirations and dispense cycles were performed (100 μl volumes each) allowing for affinity capture of RANTES proteoforms and met-RANTES. The pipettes were then rinsed with assay buffer water to remove non-specifically bounded proteins. Captured proteins were eluted directly on a 96-well formatted MALDI target using sinapic acid. Five-thousand laser shots of mass spectra were acquired from each sample spot on a Bruker’s Ultraflex III MALDI-TOF/TOF mass spectrometer. The mass spectra were externally and internally calibrated with protein standard mix and the singly and doubly charged met-RANTES signals before analysis.
In the mass spectra, several RANTES proteoforms can be detected. As shown in Figure 1, most abundant are signals representing full-length, native RANTES (1-68) and met-RANTES, along with the N-terminally cleaved RANTES proteoforms (3-68) [MW=7,663.7; missing the ‘SP’ N-terminal dipeptide, product of dipeptidyl peptidase IV (DPP IV) enzyme cleavage] and (4-68) (MW=7,500.6; missing ‘SPY’ N-terminal tripeptide). RANTES proteoforms missing N-terminal tripeptide and C-terminal dipeptide, (4-66) (MW=7,282.3) completed the dominant signals (Figure 1, top right inlet). Additional RANTES proteoforms were identified, in lower abundance and frequency: (7-66) (MW=6993.1; missing six N-terminal and two C-terminal amino acids), (4-64) (MW=7040.1; missing three N-terminal and four C-terminal amino acids), (4-65) (MW=7153.2; missing three N- and three C-terminal amino acids) and (3-66) (MW=7445.5; missing two N- and two C-terminal amino acids). The signal labelled M-RANTES with MW=7413.5 has multiple N- and C-terminal truncation possibilities, and has not been specifically assigned. The assignation of these signals was done using the observed m/z values and the program Paws, and was in accordance with previously published qualitative results [16].
All identified RANTES proteoforms were quantified using an eight-point standard curve, in the range from 1.56 to 200 ng/mL. The standard curve was constructed from the ratio of the peak intensities of the RANTES standard and the met-RANTES IRS (y-axis) versus the RANTES standard concentration (x-axis). For the analytical samples, first, the RANTES/met-RANTES peak intensity ratios for each proteoform were determined and summed up. Using the generated standard curve equation, these ratios were used to determine the total RANTES concentration in the analysed plasma sample. Then, the concentration of the individual RANTES proteoforms was calculated based on their percentage of the total RANTES. The assay was validated through several standard procedures. The intra- and inter-assay precision experiments yielded coefficients of variation of <10%. Linearity and spiking-recovery experiments produced results between 92 and 112% (observed vs expected concentration). In a final test, the results of the RANTES MSIA were compared with those obtained with commercially available ELISA using Altman–Bland plot. A good correlation, with slight positive bias (11.3%) was obtained with the native RANTES [17]. The developed MSIA for RANTES proteoforms was applied to a cohort of 297 human plasma samples. The analyses were performed on an automated platform, which enabled for a high-throughput analysis of 96 samples in a single run. Among the samples, we were able to determine the concentration and frequency of 11 RANTES proteoforms (Figure 2). The total average concentration of RANTES was found to be 44.9 ng/ml (2.15–163 ng/mL). In majority of samples, the main proteoform was the full-length, native RANTES [c(RANTES(1-68))avg=37.4 ng/mL; 1.92–132 ng/mL], followed by RANTES (3-68), [c(RANTES(3-68))avg =6.64 ng/mL; 0.138–34.4 ng/mL]. The other truncated RANTES proteoforms were present in variable frequencies in the samples, albeit at much lower concentrations (<10% of the total RANTES). Figure 2 summarizes the distribution and frequency of all 11 RANTES proteoforms. Even though majority of RANTES proteoforms were detected in only a handful of samples and in low quantities, they should be given full attention. Cleaved proteoforms have the potential to be used as indicators of an enzymatic activity, and, in turn, of changes in the metabolic homeostasis [18]. The information that this MSIA provides puts a new perspective of RANTES quantitative analysis and can be a good starting point for looking at RANTES heterogeneity in clinical context. Concluding remarks
The assay described above uses MALDI-TOF-MS to fully quantify RANTES proteoforms, and it is one of just a handful of such MALDI-based assays in existence today. The assay’s two-step approach is similar to that of well-established immunoassays, with the added benefit of MS detection as an enabling factor in differentiating the multiple proteoforms. The MALDI target is designed to accept the eluates from 96 tips at the same time, therefore making it high-throughput and time efficient (total time for RANTES assay is ~1 hour). The assay is performed on an automated platform, which limits the errors that can occur during assay execution. In review of previous and ongoing work, MSIA for RANTES performs well and introduces a new prospect and capacity for potential clinical applications in the field of biomarker discovery/rediscovery and diagnostics.
References
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13. Azenshtein E, Luboshits G, Shina S, et al. Cancer Res. 2002; 62: 1093–1102.
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16. Oran PE, Sherma ND, Borges CR, et al. Clin Chem. 2010; 56: 1432–1441.
17. Trenchevska O, Sherma ND, et al. J Proteomics 2014; 116C, 15–23.
18. Lim JK, Lu W, Hartley O, et al. J Leukoc Biol. 2006; 80: 1395–1404.
The authors
Olgica Trenchevska*1, Nisha D. Sherma1, Peter D. Reaven2, Randall W. Nelson1, Dobrin Nedelkov1
1Molecular Biomarkers, The Biodesign Institute at Arizona State University, Tempe, AZ, USA
2Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
*Corresponding author
E-mail:
olgica.trenchevska@asu.edu
Use of LC-MS/MS to measure new psychoactive substances in sewage: an application of sewage-based epidemiology
, /in Featured Articles /by 3wmediaThis contribution describes the possibility of applying liquid chromatography coupled to tandem mass spectrometry for analysing sewage in order to track down the use of new psychoactive substances.
by J. Kinyua, Prof. A. Covaci, Prof. A. L. N. van Nuijs
Introduction
Sewage-based epidemiology (SBE) is an alternative method of monitoring population drug use by the analysis of excretion products of drugs in sewage (Fig. 1). SBE has been applied since 2005 as a complementary approach to classical investigation methods, such as interviews with users, medical records, population surveys, and crime statistics for estimating illicit drug use in communities [1–3]. Data obtained from SBE provide information on drug use in a direct, quick and objective way.
New psychoactive substances (NPS) are substances that are not controlled by the 1961 United Nations Single Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances and that may pose a threat to public health [4, 5]. These compounds mimic effects of illicit drugs like cocaine, cannabis and amphetamines and are produced to evade law enforcement by introducing slight modifications to chemical structures of controlled substances [6]. Currently, more than 450 NPS are being monitored by the European Monitoring Centre for Drug and Drug Addiction (EMCDDA) with 101 new substances reported for the first time in 2014 to EU Early Warning System (EWS). Synthetic cannabinoids and synthetic cathinones are the largest groups in the NPS scene [7]. NPS are easily acquired through online vendors and in smart shops where they are sold with misleading information about their effects and safety [8]. They are considered a growing problem in many communities and are responsible for numerous fatal intoxications [9]. SBE has the potential to be usefully applied for the detection and quantification of NPS to document their occurrence to appropriate authorities. Being an emerging issue, only a few studies have applied SBE for the analysis of NPS [10–13]. In this contribution, the optimization, validation and application of an analytical method using liquid chromatography coupled to positive electrospray tandem mass spectrometry (LC–ESI-MS/MS) for the determination of seven NPS in sewage: methoxetamine (MXE), butylone, ethylone, methylone, methiopropamine, 4-methoxymethamphetamine (PMMA), and 4-methoxyamphetamine (PMA) is described together with a critical evaluation of the methodology.
LC-MS/MS methodology
An LC-MS/MS method was developed and validated using a Phenomenex Luna HILIC (hydrophilic interaction liquid chromatography) 200A (150 x 3 mm, 5 µm) column, with a mobile phase composed of A) 5 mM ammonium acetate in ultrapure water and B) acetonitrile. The mass spectrometer compound dependent parameters, fragmentor voltage and collision energy, were optimized to acquire two multiple reaction monitoring (MRM) transitions (qualifier and quantifier) for each compound, and one MRM for the internal standards (IS). The method was validated, assessing accuracy and precision, using blank sewage (samples collected prior to 2009 in which NPS have not been detected). A linear range with lower limits of quantification (LLOQ) of 0.5 ng/L (MXE and methylone) and 2 ng/L (all other compounds) and upper limits of quantification (ULOQ) of 200 ng/L was achieved for investigated compounds. The limit of detection (LOD) was between 0.02 and 0.2 ng/L for all compounds.
Sample collection and preparation
24-h composite influent sewage samples were collected from different wastewater treatment plants (WWTPs) in Belgium and one WWTP in Zurich. Before sample extraction, 50 mL sewage was filtered through a 0.7 µm glass filter to remove solid particles. After filtration, the samples were brought to pH 2 using a 6 M HCl solution and spiked with deuterated IS at a concentration of 100 ng/L. Thereafter the solid-phase extraction (SPE) procedure was performed using a mixed-mode strong cation exchange sorbent-Oasis MCX (Fig. 2).
Application of the procedure
The method could reliably differentiate the analytes and IS from endogenous components. MXE, methylone and ethylone could be detected. The method revealed the presence of MXE in sewage from five urban centres within two counties in Belgium. Methylone was detected and quantified in only two samples from Switzerland at levels slightly higher than LLOQ (Fig. 3). The compounds that were not detected could be absent in the sewage or present in the form of metabolites which were not targeted in the present study.
Advantages/limitations of the SBE methodology
Phenylethylamine-based compounds (synthetic cathinones and amphetamine-like substances) form a large group of NPS and they are very polar. Hydrophilic interaction was found to be a good and robust LC stationary phase to obtain retention for these high-polarity compounds. Furthermore, we showed for the first time in SBE that the use of a more realistic matrix for method development, such as real sewage, can help in overcoming challenges associated with matrix effects in MS detection. The results from these samples demonstrate the importance of developing highly sensitive analytical methods that can detect and quantify NPS at very low concentrations (<10 ng/L). Limitations of the present methods
It is difficult to determine if the low drug concentrations in sewage are related to low popularity of the NPS or due to the presence of an unknown form of the parent drug in sewage, urinary metabolites or transformation product from other in-sewer processes. SBE requires a specific, reliable and stable biomarker for the NPS of interest. Further studies on the metabolism and in-sewer transformation processes (which may affect stability of drug residues) of NPS needs thus to be carried out to provide SBE with information regarding additional biomarkers of NPS parent drugs.
Future of SBE in NPS analysis
Concentrations of NPS in sewage may be low depending on the area served by the WWTP and on the prevalence of its use [14]. Therefore, pooled urine analysis would be useful in detecting the occurrence of NPS before dilution into sewage [15]. It would be a valuable approach to combine pooled urine analysis and SBE to track down the actual use of NPS in communities.
Conclusion
In conclusion, SBE can help in revealing the occurrence of NPS within catchment areas of urban centres and showed the need to develop very sensitive analytical methods to detect NPS in sewage.
References
1. Bijlsma L, Sancho JV, Pitarch E, et al. Simultaneous ultra-high-pressure liquid chromatography-tandem mass spectrometry determination of amphetamine and amphetamine-like stimulants, cocaine and its metabolites, and a cannabis metabolite in surface water and urban wastewater. J Chromatogr A 2009; 1216: 3078–3089.
2. Boleda MR, Galceran MT, Ventura F. Trace determination of cannabinoids and opiates in wastewater and surface waters by ultra-performance liquid chromatography-tandem mass spectrometry. J Chromatogr A 2007; 1175: 38–48.
3. Huerta-Fontela M, Galceran MT, Ventura F. Ultraperformance liquid chromatography-tandem mass spectrometry analysis of stimulatory drugs of abuse in wastewater and surface waters. Anal Chem 2007; 79: 3821–3829.
4. United Nations Office on Drugs and Crime (UNODC). Global synthetic drugs assessment. (United Nations publication, Sales No. E.14.XI.6), 2014. http://www.unodc.org/documents/scientific/2014_Global_Synthetic_Drugs_Assessment_web.pdf
5. King LA, Kicman AT. A brief history of ‘new psychoactive substances’. Drug Test Anal. 2011; 3: 401–403.
6. Dargan PI, Wood DM. Novel psychoactive substances classification, pharmacology and toxicology. Elsevier/Academic Press, 2013. ASIN: B00FK8HYY2.
7. European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). New psychoactive substances in Europe. An update from the EU Early Warning System, 2015. http://www.emcdda.europa.eu/publications/2015/new-psychoactive-substances
8. EMCDDA. EMCDDA–Europol 2013 Annual Report on the implementation of Council Decision 2005/387/JHA, 2014. http://www.emcdda.europa.eu/publications/implementation-reports/2013
9. Vevelstad M, Øiestad E.L, Middelkoop G, et al. The PMMA epidemic in Norway: Comparison of fatal and non-fatal intoxications. Forensic Science International 2012; 219: 151–157.
10. Kinyua J, Covaci A, Maho W, et al. Sewage-based epidemiology in monitoring the use of new psychoactive substances: validation and application of an analytical method using LC-MS/MS. Drug testing and analysis 2015 ( In press).
11. Reid M.J, Derry L, Thomas K.V. Analysis of new classes of recreational drugs in sewage: Synthetic cannabinoids and amphetamine-like substances. Drug Test Anal. 2014; 6: 72–79.
12. Van Nuijs ALN, Gheorghe A, Jorens PG, et al. Optimization, validation, and the application of liquid chromatography-tandem mass spectrometry for the analysis of new drugs of abuse in wastewater. Drug Test Anal. 2014; 6: 861–867.
13. Kankaanpää A, Ariniemi K, Heinonen M, et al. Use of illicit stimulant drugs in Finland: a wastewater study in ten major cities. Sci Total Environ. 2014; 487: 696–702.
14. Archer JRH, Dargan PI, Lee HMD, et al. Trend analysis of anonymised pooled urine from portable street urinals in central London identifies variation in the use of novel psychoactive substances. Clinical Toxicol (Phila). 2014; 52: 160–165.
15. Archer JRH, Dargan PI, Hudson S, et al. Analysis of anonymous pooled urine from portable urinals in central London confirms the significant use of novel psychoactive substances. QJM 2013; 106: 147–152.
The authors
Juliet Kinyua MSc, Adrian Covaci PhD, Alexander L.N. van Nuijs* PhD
Toxicological Center, University of Antwerp, Belgium
*Corresponding author
E-mail: alexander.vannuijs@uantwerpen.be
A three-point case for clinical labs to adopt LC-MS/MS technology
, /in Featured Articles /by 3wmediaLiquid chromatography-mass spectrometry (LC-MS/MS) is one of the most promising diagnostic technologies in the in-vitro diagnostics industry, but it is not yet widely adopted by mainstream laboratories. An estimated five percent of LC-MS/MS instruments reside in truly clinical diagnostic settings while the majority are deployed in research and reference laboratories.
by Dr Bori Shushan
There are three compelling reasons for clinical labs to incorporate LC-MS/MS solutions into their routine operations:
Testing quality
Direct measurement technology is more specific and can address the limitations inherent to immunoassay testing. In particular, for small-molecule analyte testing, immunoassay results can be elevated due to the presence of metabolites from other drugs with core structures that are similar to the targeted analyte.
Workflow efficiency
As LC-MS/MS solutions are typically found in specialty laboratories, most clinical labs must outsource certain tests. Transporting samples adds complexity, cost, and time to the testing process. In drugs of abuse testing for example, patients are initially screened using immunoassay and then confirmed using LC-MS/MS. Having this capability within the laboratory can provide quick turnaround times for faster diagnosis and treatment for patients. LC-MS/MS methods can also test for multiple analytes simultaneously where immunoassay methods require a separate test for each analyte.
Meeting market demand
The market demand that LC-MS/MS addresses arises from trends such as the growing use of opiates and increasingly more stringent regulations. On-site LC-MS/MS testing can deliver both qualitative and quantitative accuracy and precision to help clinicians understand the actual consumption of abused and or prescription drugs.
While the reasons for adopting LC-MS/MS are compelling, there are logistical and regulatory barriers to entry rooted in the current state of LC-MS/MS automation. LC-MS/MS processes are automated to some degree, but the entire process must be improved. Workflows still require many manual steps, including sample preparation and data entry into LIMS systems. This takes labour and time and can lead to errors, all of which are unacceptable to regulatory bodies and laboratory managers. The industry recognizes that innovative solutions are required to address these analytical challenges; however, only when LC-MS/MS achieves the rigorous engineering and quality developments required for regulatory approval will more labs be allowed to adopt this gold standard technology and make a meaningful advancement in diagnostic testing.
Workflow transformed : the DxN VERIS fully automated system for molecular diagnostics in the EU
, /in Featured Articles /by 3wmediaDelegates at EuroMedLab 2015, held in Paris 21st-25th June, attended the Beckman Coulter Molecular symposium ‘Workflow Transformed’. Chairman Jacques Izopet (Toulouse) introduced the importance of molecular diagnostics in providing faster, reliable results, enabling improved patient management while saving laboratory time allowing redistribution of staff and resources for research and innovation.
Andrew Williams, (Nexus Global Solutions) explained how the company conducted a multi-site time/motion workflow analysis study comparing Beckman Coulter’s DxN VERIS Molecular Diagnostics System to existing batch and semi-automatic molecular diagnostic platforms. During two 4-day studies conducted in Sheffield (UK) and Barcelona (Spain) in May 2015, the systems were run in tandem, and the study focused on key areas including time to result, hands on time, and maintenance requirements.
Jordi Vila, (Barcelona) identified how his laboratory needed workflow improvements to reduce waste, increase efficiency, maximize use of personnel and equipment, and to reduce the potential for errors. “Currently molecular diagnostics are undertaken on three platforms, with specific assays only run on certain days of the week,” Vila commented. “DxN VERIS would take up less space in our cramped laboratory and allow up to 20 assays to be run at any time. Batching would not be required as samples can be added as they arrive, and results are available as samples are running. This is a great advantage; using existing systems we have to wait until the end of the run.”
“The VERIS simplified workflow, reduced the number of steps required from sample preparation to result from 29 to just 11,” Vila continued, “we also made savings in maintenance time and reagent/consumables. Assay reagents are stored on board for up to 14 days, and, as shown in figure 1, only four consumables are required, comparable systems need up to 20 or more.”
“We reduced hands-on time for HIV-1 testing; DxN VERIS took approximately half the time to result against other systems. We experienced workflow advantages via continuous loading; use of universal tube racks; true single sample random access; the ability to add urgent samples, and test multiple target viruses at any time. We could save space with the need for only one instrument, together with more economical use of laboratory staff because no pipetting is required,” confirmed Vila.
Duncan Whittaker, (Sheffield, UK) explained that the study undertaken at his site involved comparison of DxN VERIS with three other systems. “Workloads within our department have increased by 57% in the last 3 years”, Whittaker reported. “Faced with this challenge, together with competition from other private and public laboratories and the loss of experienced staff, workflow improvements and efficient utilization of both staff and resources are key”.
“Currently we use three systems, housed in different rooms, so a lot of staff time is spent moving from one instrument to another. Complexity of use was studied and with these systems we found that 29 or 30 steps were required, but only 11 steps are needed with VERIS”, Whittaker continued (figure 2).
“When you combine this with the ease of use – it only took 20 minutes to train staff to use the equipment – and rapid time to result and the ability to run multiple targets on one system, we could offer significant benefits to clinicians, allowing them to deliver better patient management. Just to cite HBV as an example, we would normally do 3-4 extractions over a couple of days before batching on to the next system, so results would take several days. With VERIS we can offer same day results.”
“Feedback from renal transplant and renal dialysis departments has shown that the true single sample random access mode and rapid time to result would greatly benefit the way patients are seen and treated in clinics. For example, with current systems, dialysis patients returning from abroad need to attend two hospital visits to confirm negative status, but with VERIS we could reduce this to one visit – an immediate benefit for the patient who may have had to travel many miles to get to us,” Whittaker concluded.
For further information about these studies, DxN VERIS Molecular Diagnostics System and the DxN VERIS assays currently available, please contact: Tiffany Page, Senior Pan European Marketing Manager Molecular Diagnostics,
Email: info@beckmanmolecular.com
or visit: www.beckmancoulter.com/moleculardiagnostics
Vacuette Safety Products
, /in Featured Articles /by 3wmediaSyndromic infectious disease testing in about an hour
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, /in Featured Articles /by 3wmediaIntroducing the DxN VERIS Molecular Diagnostics
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