Alterations of the microbiome are associated with colorectal cancer. Research suggests that microbiome data could improve colorectal cancer screening. Analysis of the microbiome directly from existing screening methods offers the opportunity to rapidly translate this research into practice, with the potential to develop a multifactorial colorectal cancer screening tool.
by Dr Caroline Young and Professor Philip Quirke
Current colorectal cancer screening methods
Different countries have adopted various approaches to colorectal cancer screening. They share a common goal: detection of asymptomatic adenomas or early stage carcinomas, as detection and treatment at an earlier stage is associated with improved survival [1]. Two main screening methods are in use: detection of fecal occult blood and visualization of the colon. Stool DNA testing has recently been approved but is currently prohibitively expensive.
Detection of fecal occult blood can be achieved using the guaiac fecal occult blood test (gFOBT) or an immunochemical method, fecal immunochemical test (FIT). The gFOBT method requires participants to apply stool to a gFOBT card on three occasions and return this to a screening centre through the post. Hydrogen peroxide is applied and if heme is present, blue discolouration occurs. This method has been shown to reduce mortality by 16 % [2]. The FIT method requires participants to insert a FIT probe into stool and return this to a screening centre through the post. An antibody-based assay is used to detect globin. FIT is more sensitive and specific, can be analysed quantitatively and has improved acceptability [3]. Participants in whom fecal occult blood is detected above a threshold, by either method, are referred for colonoscopy.
Alternatively, direct visualization of the colon by colonoscopy/sigmoidoscopy can be undertaken as first-line screening. Limitations include procedural risks, associated costs, workforce capacity and reduced acceptability [4].
The microbiome and colorectal cancer
The microbiome can be characterized using a number of technologies: next generation sequencing (NGS) of bacterial 16SrRNA, whole genome shotgun metagenomics of bacterial communities or the analysis of fecal metabolites (metabolomics). These techniques have enabled an appreciation of the diversity and function of the microbiome in health and disease.
Epidemiological studies demonstrate that the incidence of colorectal cancer is highest in countries with a Western culture, which encompasses Western diet, sanitation and hygiene, medication use, urbanization, etc. [5]. Migrant populations to such countries acquire the increased risk, suggesting an environmental risk factor. African Americans, who typically have a high incidence of colorectal cancer, have been shown to have different microbiomes to Native Africans, who have a low incidence of colorectal cancer [6] and the diets typical of these two groups have been shown to differentially influence the microbiome [7].
Numerous studies have found differences in the microbiome, ‘dysbiosis’, of patients with colorectal adenomas or carcinomas compared to healthy controls [8]. In general, dysbiosis is characterized by a decrease of short chain fatty acid-producing bacteria, an increase of bacteria that produce bile salts or hydrogen sulphide, an increase of pathogenic bacteria and inflammation [9]. In particular, the species Fusobacterium nucleatum, a Gram-negative oral commensal, has been associated with colorectal carcinoma in many studies.
Animal models have explored potential mechanisms [10] and interestingly show that risk is transferable with transplant of dysbiotic microbiomes. This suggests that dysbiosis may be causative or promotional of the development of colorectal cancer, rather than merely associative.
Given the association between dysbiosis and colorectal cancer, researchers have considered whether the microbiome could be used as a screening tool.
The microbiome compared to gFOBT
Several studies have compared the accuracy of the microbiome as a screening tool to gFOBT. Amiot et al. showed that a screening model combining age plus microbiome (typed by qPCR) was no better than a model combining age plus gFOBT [11]. However, metabolomic analysis [by 1(H)-NMR spectroscopy] was more accurate than gFOBT [12]. Zeller et al. created a screening model that combined metagenomic data with gFOBT results, which lead to an increase in sensitivity compared to gFOBT alone. This model was subsequently validated in a cohort of a different nationality. It showed some ability to distinguish colorectal cancer from a distinct bowel condition (inflammatory bowel disease) and could be extrapolated to NGS of 16SrRNA (a cheaper method) [13].
Zackular et al. used 16SrRNA analysis of the microbiome to create models combining microbiome data and patient metadata that were more accurate than models based on metadata alone [14]. A model comprising BMI, microbiome data and gFOBT was more accurate at distinguishing adenoma from carcinoma than gFOBT alone. Yu et al. used metagenomics to identify two discriminatory bacterial genes that they then validated as biomarkers by qPCR (a cheaper method) in a cohort of a different nationality. The area under the receiver operating characteristic (ROC) curve for discriminating carcinoma from controls was 0.84, although gFOBT or FIT screening was not performed for comparison [15].
The microbiome compared to FIT
As FIT is replacing gFOBT in many screening programmes and has a higher sensitivity, comparing the accuracy of the microbiome as a screening tool with FIT is more appropriate.
Baxter et al. used 16SrRNA to create a screening model that combined microbiome data and FIT to discriminate healthy controls from cases with either adenoma or carcinoma [16]. This model was more sensitive but less specific than FIT alone; it detected 70% of cancers and 37% of adenomas which were missed by FIT. Liang et al. [17] identified four bacterial species (one being F. nucleatum) by qPCR that could distinguish colorectal carcinoma from healthy controls with greater accuracy than FIT. Combining microbiome and FIT data afforded greater accuracy still.
Goedert et al. [18] analysed the microbiome by 16SrRNA in patients with a positive FIT result at baseline. The microbiome data gave an area under the ROC curve for discriminating between healthy controls and colorectal adenoma of 0.767.
Limitations of current research
The studies mentioned above show promise for the microbiome as a potential colorectal cancer screening tool. However, they should be interpreted with a degree of caution, owing to a number of limitations which mean that aspects of the studies do not realistically reflect screening conditions. Several of the studies assessed participants at increased risk of colorectal cancer or who were symptomatic. Some collected stool samples following bowel preparation and colonoscopy; one study found that this did not affect the significance of results [16], whereas another found that it did [15]. Several studies included adenomas <10 mm within their control groups. Many of the studies created models that distinguished adenomas from carcinomas or carcinomas from healthy controls; few designed models to discriminate between healthy controls and participants with any colorectal lesion (i.e. either adenoma or carcinoma).
All of the studies used whole stool samples that were refrigerated or frozen by participants at home or delivered within a limited time window to research centres. This method of sample collection would not translate to national screening programmes, which already struggle with poor participant uptake. In light of this, researchers have, therefore, investigated whether the microbiome can be analysed directly from the existing screening tools, gFOBT or FIT.
Analysing the microbiome directly from existing screening tools
Sinha et al. emphasize the need to assess reproducibility, stability over time and how accurately results reflect the gold standard (fresh or immediately frozen stool) when analysing different methods of microbiome sample collection [19]. They found that 16SrRNA microbiome results were similar when analysed from unprocessed or processed gFOBT cards and, in addition to Dominianni et al. [20], showed stability after storage at room temperature for several days. This work was extended by Taylor et al. [21] who demonstrated that the microbiome is stable when analysed by 16SrRNA from processed gFOBT cards stored at room temperature for up to 3 years.
Lotfield et al. showed that metabolomic assessment of the microbiome by ultra-performance liquid chromatography and high resolution/tandem mass spectrometry was stable and accurate (albeit with a degree of bias affecting certain metabolite groups) when analysed directly from gFOBT samples but not from FIT samples [22]. This suggests that different methods of sample collection may be more or less appropriate dependent upon the method of microbiome analysis.
These studies have assessed methods of microbiome sample collection from healthy volunteers. Baxter et al. [23] have analysed the microbiome directly from processed FIT from subjects with normal bowels, colorectal adenomas or carcinomas. Their study comes with the caveat that some of the stool samples were collected after bowel preparation and colonoscopy; samples were stored at −80 °C before being thawed and transferred to FIT; FIT was refrigerated for up to 2 days, processed, then stored at −20 °C before being thawed for microbiome analysis. The study demonstrated that a screening model to discriminate between healthy controls and subjects with any colonic lesion had a similar area under the ROC curve whether microbiome analysis was performed directly from FIT samples or whole stool samples.
As an alternative to stool, Westenbrink et al. analysed microbiome-related volatile organic compounds from urine [24] and described a similar sensitivity for the detection of colorectal cancer as gFOBT or FIT.
Conclusion
Research suggests that there is potential for microbiome analysis to both augment and to be integrated with existing screening methods. The landscape of colorectal cancer screening is changing [25]; it seems likely that a more sophisticated, multifactorial screening tool will be adopted. Microbiome analysis is likely to contribute and may even offer information beyond that of screening, e.g. prevention or treatment targets [26]. Furthermore, collection of longitudinal, population-based microbiome data via national screening programmes will transform the field of microbiome research.
References
1. Cancer Research UK (http://www.cancerresearchuk.org).
2. Hewitson P, Glasziou PP, Irwig L, Towler B, Watson E. Screening for colorectal cancer using the faecal occult blood test, Hemoccult. Cochrane Database Syst Rev. 2007; DOI: 10.1002/14651858.CD001216.pub2
3. Schreuders EH, Grobbee EJ, Spaander MC, Kuipers EJ. Advances in fecal tests for colorectal cancer screening. Curr Treat Options Gastroenterol. 2016; 14(1): 152–162.
4. US Preventive Services Task Force, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW Jr, García FA, Gillman MW, Harper DM, et al. Screening for colorectal cancer: US preventive services task force recommendation statement. JAMA 2016; 315(23): 2564–2575.
5. Haggar FA, Boushey RP. colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg. 2009; 22(4): 191–197.
6. Ou J, Carbonero F, Zoetendal EG, DeLany JP, Wang M, Newton K, Gaskins HR, O’Keefe SJ. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am J Clin Nutr. 2013; 98(1): 111–120.
7. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014; 505(7484): 559–563.
8. Borges-Canha M, Portela-Cidade JP, Dinis-Ribeiro M, Leite-Moreira AF, Pimentel- Nunes P. Role of colonic microbiota in colorectal carcinogenesis: a systematic review. Rev Esp Enferm Dig. 2015; 107(11): 659–671.
9. Sun J, Kato I. Gut microbiota, inflammation and colorectal cancer. Genes Dis. 2016; 3(2): 130–143.
10. Keku TO, Dulal S, Deveaux A, Jovov B, Han X. The gastrointestinal microbiota and colorectal cancer. Am J Physiol Gastrointest Liver Physiol. 2015; 308(5): G351–363.
11. Amiot A, Mansour H, Baumgaertner I, Delchier JC, Tournigand C, Furet JP, Carrau JP, Canoui-Poitrine F, Sobhani I; CRC group of Val De Marne. The detection of the methylated Wif-1 gene is more accurate than a fecal occult blood test for colorectal cancer screening. PLoS One 2014; 9(7): e99233.
12. Amiot A, Dona AC, Wijeyesekera A, Tournigand C, Baumgaertner I, Lebaleur Y, Sobhani I, Holmes E. (1)H NMR spectroscopy of fecal extracts enables detection of advanced colorectal neoplasia. J Prot Res. 2015; 14(9): 3871–3881.
13. Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Böhm J, Brunetti F, et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol. 2014; 10: 766.
14. Zackular JP, Rogers MA, Ruffin MT 4th, Schloss PD. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res (Phila). 2014; 7(11): 1112–1121.
15. Yu J, Feng Q, Wong SH, Zhang D, yi Liang Q, Qin Y, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 2015; DOI: 10.1136/gutjnl-2015-309800.
16. Baxter NT, Ruffin MT 4th, Rogers MA, Schloss PD. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med. 2016; 8(1): 37.
17. Liang JQ, Chiu J, Chen Y, Huang Y, Higashimori A, Fang JY, Brim H, Ashktorab H, Ng SC, et al. Fecal bacteria act as novel biomarkers for non-invasive diagnosis of colorectal cancer. Clin Cancer Res. 2016; DOI: 10.1158/1078-0432.CCR-16-1599.
18. Goedert JJ, Gong Y, Hua X, Zhong H, He Y, Peng P, Yu G, Wang W, Ravel J, et al. Fecal microbiota characteristics of patients with colorectal adenoma detected by screening: a population-based study. EBioMedicine 2015; 2(6): 597–603.
19. Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, Flores R, Sampson J, Knight R, Chia N. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(2): 407–416.
20. Dominianni C, Wu J, Hayes RB, Ahn J. Comparison of methods for fecal microbiome biospecimen collection. BMC Microbiol. 2014; 14: 103.
21. Taylor M, Wood H, Halloran S, Quirke P. Examining the potential use and long term stability of guaiac faecal occult blood test cards for microbial DNA 16srRNA sequencing. J Clin Pathol. Accepted for publication.
22. Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, Chia N, Sinha R. Comparison of collection methods for fecal samples for discovery metabolomics in epidemiologic studies. Cancer Epidemiol Biomarkers Prev. 2016; 25(11): 1483–1490.
23. Baxter NT, Koumpouras CC, Rogers MA, Ruffin MT 4th, Schloss P. DNA from fecal immunochemical test can replace stool for microbiota-based colorectal cancer screening. Microbiome 2016; 4(1): 59.
24. Westenbrink E, Arasaradnam RP, O’Connell N, Bailey C, Nwokolo C, Bardhan KD, Covington JA. Development and application of a new electronic nose instrument for the detection of colorectal cancer. Biosens Bioelectron. 2015; 67: 733–738.
25. Nguyen MT, Weinberg DS. Biomarkers in colorectal cancer screening. J Natl Compr Canc Netw. 2016; 14(8): 1033–1040.
26. Pitt JM, Vetizou M, Waldschmitt N, Kraemer G, Chamaillard M, Boneca IG, Zitvogel L. Fine-tuning cancer immunotherapy: optimizing the gut microbiome. Cancer Research 2016; 76(16): 4602–4607.
The authors
Caroline Young* MA, BMBCh; Philip Quirke BM, PhD, FRCPath, FMedSci
Wellcome Trust Brenner Building, St James University Hospital, Leeds LS9 7TF, UK
*Corresponding author
E-mail: caroline.young4@nhs.net
Anti-glycolipid antibodies: their role in neurological disease and their detection
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaby Carrie A. Chadwick The relationship between anti-glycolipid antibodies and peripheral neuropathies has for many years been studied by laboratory investigations because of the antibody-phenotype associations identified. This has led to the discovery of a number of peripheral nerve conditions affected by the presence of anti-glycolipid antibodies. This article describes some of the conditions associated […]
Computer-aided immunofluorescence microscopy in autoimmune diagnostics
, /in Featured Articles /by 3wmediaIndirect immunofluorescence (IIF) is an indispensable method for autoantibody diagnostics, providing high sensitivity and specificity together with a broad antigenic spectrum. However, the microscopic evaluation of the fluorescence patterns is both time-consuming and challenging for laboratory staff, and is, moreover, based on subjective interpretation. Laboratories are increasingly turning to automated systems to facilitate and standardize the IIF readout and interpretation. In recent years various automation systems have been developed, which provide automated digital acquisition of IIF images, discrimination of positive and negative samples, as well as pattern classification for key applications. This article focuses on the EUROPattern system, which provides computer-aided immunofluorescence microscopy for anti-nuclear antibodies (ANA), anti-neutrophil granulocyte cytoplasm antibodies (ANCA), antibodies against double-stranded DNA (anti-dsDNA) on Crithidia luciliae, monospecific antigen microdots (EUROPLUS) and transfected cell-based assays e.g. for anti-neuronal antibodies. The accuracy of automated evaluation compared to visual assessment has been investigated in various published studies.
by Dr Jacqueline Gosink
ANA
ANA represent a key diagnostic criterion for many autoimmune diseases, including systemic lupus erythematosus (SLE), mixed connective tissue disease, Sjögren’s syndrome, systemic sclerosis, polymyositis, dermatomyositis and primary biliary cirrhosis. The gold standard for ANA determination is IIF on human epithelial (HEp-2) cells. This substrate provides the complete antigen spectrum and allows investigation of over 100 different autoantibodies. Observation of the fluorescence pattern enables classification of the antibody or antibodies present in the patient sample. Positive results are confirmed by monospecific tests such as ELISA, immunoblot or IIF microdot assays.
The automated evaluation of HEp-2 cells includes reliable discrimination of positive and negative ANA results, as well as classification of all ANA patterns [1] (Figure 1), encompassing homogeneous, speckled, nuclear dots, nucleolar, centromeres, nuclear envelope and cytoplasmic. The ANA patterns identified by EUROPattern correspond to the competent level reporting defined by the International Consensus on ANA Patterns (ICAP; www.anapatterns.org). Mixed patterns, which occur when more than one antibody is present, are also recognized and reported as such. The pattern is assigned by analysing its features and comparing it to a reference database of over 5000 images, corresponding to 115,000 cells. Unspecific signals originating from outside of the cells are identified by means of a DNA counterstain and subsequently rejected. The evaluation also includes titre designations with confidence values for the detected antibodies. Results from the HEp-2 screening can be monospecifically confirmed using microdot substrates of purified antigens, which are incubated and evaluated in parallel.
To assess the diagnostic accuracy, the automated evaluation was compared to conventional visual interpretation by experts in the field using 351 patient sera [2]. The concordance for positive/negative discrimination was 99%, with an analytical sensitivity of 100% and a specificity of 98%. In 60% of samples, the pattern, including variable mixed patterns, was recognized completely by the software. In 94% of samples, the main pattern was correctly designated. A further study showed 79% correct pattern assignment.
Anti-dsDNA antibodies
Anti-dsDNA antibodies are a hallmark of SLE and represent an important criterion for diagnosis. Their prevalence in SLE ranges from 30% to 98% in different studies, depending among other things on the test method used. Like the gold standard Farr assay, IIF using Crithidia luciliae as the substrate (CLIFT) is considered to have a very high disease specificity. The method takes advantage of the kinetoplast of C. luciliae, which is rich in DNA but contains hardly any other antigens, thus enabling highly selective detection of anti-dsDNA antibodies. However, manual reading of the fluorescence signals is subjective and leads to high intra- and inter-laboratory variation, making standardized automated evaluation a desirable goal.
Automated interpretation of CLIFT has recently been incorporated into the EUROPattern system [3] (Figure 2). The software is able to recognize the organelles of the protozoan and evaluates the specific kinetoplast fluorescence rather than just dark-light classification, increasing the reliability of the evaluation. Results are classified as positive or negative, and include a titre designation based on the fluorescence intensity.
In a clinical study, automated and visual evaluation of C. luciliae IIF was compared using 569 consecutive sera submitted for routine anti-dsDNA screening and 100 sera from healthy blood donors. The automated system recognized all 73 of the anti-dsDNA positive samples identified by the visual evaluation. Moreover, 93% of the titre designations were concordant. The overall sensitivity of the system amounted to 100% with a high specificity of 97%. Compared to visual microscopy the overall accuracy was 97%.
ANCA
ANCA are important serological markers for diagnosis and differentiation of autoimmune vasculitides, especially granulomatosis with polyangiitis (GPA, formally known as Wegener’s granulomatosis), which is characterized by autoantibodies against proteinase 3 (PR3), and microscopic polyangiitis, which is typified by autoantibodies against myeloperoxidase (MPO). In addition, ANCA can be found in chronic inflammatory bowel diseases. ANCA are detected by IIF with monospecific confirmation using ELISA, immunoblot or IIF microdot assays.
The IIF substrates ethanol-fixed and formalin-fixed granulocytes are used to identify the typical ANCA staining patterns of anti-PR3 (cytoplasmic, cANCA) and anti-MPO (perinuclear, pANCA) antibodies. An additional substrate consisting of HEp-2 cells coated with granulocytes allows immediate differentiation between ANCA and ANA, while purified antigen microdots of PR3, MPO or glomerular basement membrane (GBM) antigen provide simultaneous monospecific antibody characterization. The different substrates are incubated and automatically evaluated in parallel as BIOCHIP mosaics, thus providing ANCA screening and confirmation in one step.
Evaluation software such as EUROPattern provides automated positive/negative discrimination of samples, as well as recognition of pANCA and cANCA patterns [1] (Figure 3). Further pattern constellations such as DNA-ANCA (atypical pANCA, xANCA), which can arise from antibodies against lactoferrin or other antigens, are also taken into account by the software. The automated system proposes a result based on the recognized cellular patterns and the results on the antigen microdots. An estimated titre with a confidence value is given.
Anti-neuronal antibodies
Neuronal cell-surface autoantibodies occur in autoimmune encephalitis and their detection can secure an early diagnosis, enabling immediate treatment which is critical for patient outcome. In recent years a considerable number of novel target antigens has been discovered, for example, glutamate receptors of type NMDA and AMPA, GABAB receptors, voltage-gated potassium channel-associated proteins LGI1 and CASPR2, DPPX and IgLON5.
Diagnostic tests for the new parameters are based on recombinant-cell (RC) IIF, in which transfected cells expressing the relevant antigen are used for monospecific antibody detection. This test method enables authentic presentation of the fragile membrane-associated surface antigens. Since many of the autoantibody markers are rare and do not always overlap, a multiparametric screening using BIOCHIP mosaics made up of different substrates is recommended. Results for RC-IIF assays can be evaluated automatically using a newly developed function of EUROPattern. The system automatically takes digital images of the substrates and provides a positive/negative classification.
The quality of the acquired images was assessed by comparing on-screen appraisal with visual microscopy using 753 incubations of numerous serum samples sent to a clinical immunology laboratory [4]. Ambiguous fluorescence signals detected at the microscope were excluded to avoid inter-reader deviations. The two evaluation strategies revealed a concordance of 100% with respect to positive/negative discrimination, confirming the high quality of the images.
Arbovirus antibodies
Immunofluorescence microscopy is also useful for infectious disease diagnostics. For example, infections with Zika virus, dengue virus and chikungunya virus are difficult to tell apart clinically as they manifest with similar symptoms and are endemic in much the same regions. Serological tests are an important diagnostic method, especially beyond the short viremic phase when direct detection is no longer effective. Viral antibodies can be detected by IIF using virus-infected cells. However, cross reactions between flavivirus antibodies can occur.
A BIOCHIP mosaic comprising substrates for Zika, dengue and chikungunya viruses enables parallel antibody determination, aiding clarification of cross reactivities and supporting differential diagnosis. The substrates can be evaluated semi-automatically using the digital image acquisition function of EUROPattern. In particular, inspection of the images side-by-side on the computer screen considerably facilitates the interpretation.
Fully automated immunofluorescence microscopy
Computer-aided fluorescence microscopy can be further standardized and facilitated through use of complementary hardware. The EUROPattern microscope (Figure 4) has been tailored to the requirements of immunofluorescence. Next to the high-precision optical system, it has a controlled LED, which maintains a constant light flux, ensuring highly reproducible results. The cLED has an extremely long life span without maintenance (over 50,000 hours) and low power consumption, ensuring cost-effectiveness for laboratories. The microscope is equipped with a slide magazine which can process up to 500 analyses in succession within 2.5 hours (18 seconds per field), correctly identifying the slides by means of matrix codes.
Results from the automated IIF evaluation can be viewed and validated directly at the computer screen, enabling a diagnosis to be established quickly and efficiently. The high-resolution images are sharply focused with the aid of a counterstain. The counterstain also serves to verify correct performance of the incubation. Negative results can be verified in batches, while positive samples can be individually checked and confirmed by the medical technologist. Results from different serum dilutions and substrates are consolidated into one report per patient, and new findings are compared with previous records. Final results can be signed electronically and forwarded at a click.
Perspectives
The need for standardization and automation in IIF is tremendous in all fields of autoimmune diagnostics. In particular, manual evaluation of results is time consuming and subjective. Automation platforms with harmonized software and hardware components have in recent years contributed enormously to the standardization and simplification of the evaluation process, especially for ANA, ANCA and CLIFT. Advanced software provides positive/negative classification, pattern recognition and titre designation at a quality equivalent to visual microscopy. The recording of tissue substrates, such as liver, kidney, stomach, esophagus, small intestine, heart and neuronal tissue, is also feasible. Future development will focus on the recognition of organ- and non-organ-specific autoantibodies on tissues, for example antibodies against mitochondria, epithelial membrane, epidermal basement membrane, desmosomes, heart muscle and neuronal antigens. The continued development of automated evaluation systems is anticipated to lead to even greater standardization of IIF and further reductions in workflow for diagnostic laboratories.
References
1. Krause et al. Lupus 2015: 24: 516-29
2. Voigt et al. Clin. Devel. Immunol. 2012: vol 2012, article ID 651058
3. Gerlach et al. J. Immunol. Res. 2015: vol 2015, article ID 742402
4. Fraune et al. Autoimmunity Reviews 15 (2016) 937-942
The author
Jacqueline Gosink, PhD
EUROIMMUN AG, Seekamp 31, 23560 Luebeck, Germany
E-mail: j.gosink@euroimmun.de
Autoimmune diagnostics by immuno- fluorescence: variability and harmonization
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaby Dr Petraki Munujos The antinuclear antibodies (ANA) determination is one of the most commonly used techniques in the autoimmunity clinical laboratory. Far from being outdated, indirect immunofluorescence (IF) is a powerful laboratory tool not only for clinical diagnostics, but for disease follow-up and prognosis estimation as well. Unlike other more precise quantitative techniques, IF […]
Evaluation of UriSed 3 automated urine microscopy analyser
, /in Featured Articles /by 3wmediaUrinalysis may provide evidence of significant renal disease in asymptomatic patients. The microscopic urinalysis is vital to making diagnoses in many asymptomatic cases, including urinary tract infection, urinary tract tumors, occult glomerulonephritis, and interstitial nephritis.
Presence or absence of different particles in urine sediment is crucial for clinical decision making. Urine sediment cells or particles provide important information for the diagnosis of renal or urinary diseases [1]. The patented UriSed Technology was developed to reduce the shortcomings of manual microscopy through automation. [2]. The UriSed analysers provide a reliable and reproducible solution since 2007 [3]. The new generation instrument based on the improved UriSed Technology, UriSed 3 was introduced in the market in 2015. UriSed 3 is an automated urine microscopy analyser with a revolutionary particle visualization utilizing both bright-field and phase-contrast microscopy. In the present study, we evaluated the analytical performance of UriSed 3 Automated Urine Microscopy Analyser (Manufactured by 77 Elektronika Kft., Budapest) and compared the results to those from manual microscopy using standardized KOVA counting chambers.
UriSed 3 provides quantitative Red Blood Cell (RBC) and White Blood Cell (WBC) results, and semi-quantitative results for all other particle types: WBC Clumps (WBCc), Squamous Epithelial Cells (EPI), Non-squamous Epithelial Cells (renal tubular and urothelium cells) (NEC), Crystals (CRY): Calcium oxalate dihydrate (CaOxd), Calcium oxalate monohydrate (CaOxm), Uric acid (URI), and Triple-phosphate crystals (TRI), Hyaline casts (HYA), Pathological casts (PAT), Bacteria (cocci-like and rod-like) (BACc, BACr), Yeasts (YEA), Spermatozoa (SPRM) and Mucus (MUC) [4].
Phase-contrast microscopy by UriSed 3
Phase-contrast microscopy is an optical microscopy technique that converts phase shifts in light passing through a transparent specimen to brightness changes in the image. Phase shifts themselves are invisible, but become visible when shown as brightness variations. In particular, for urinary sediment examination, phase-contrast supplies an optimal identification of particles with a low refractive index (e.g., hyaline casts and RBC devoid of their hemoglobin content, the so-called “ghost RBC”) and of cellular morphological details) [9, 10]. Therefore the use of phase-contrast microscopy is encouraged also by international guidelines on urinalysis [5, 6].
The measurement technique of the UriSed 3 instrument is a combination of a bright-field microscope and a phase-contrast microscope in one optical system. Preparation of the UriSed 3 analyser for measurement takes only a few minutes. It needs distilled water for washing its pipette, and patented disposable plastic cuvettes for sample investigation. The instrument throughput is up to 120 samples per hour. The whole measurement process is completely automatic: 200 µl of urine sample is dispensed into the cuvette, then spinning the cuvette for a few seconds gently deposits formed elements into a monolayer at the bottom of the cuvette. The built-in digital camera takes and saves both a bright-field and a phase-contrast microscopic image from the same view-field at 15 different positions of the sediment layer. Information from both whole view-field images are evaluated by a neural network based image processing software.
Material and methods
Analysis of 311 samples was performed to evaluate UriSed 3 analytical performance compared to the manual microscopy urine examination method. Both measurements were carried out with the same anonymous urine samples. Fresh, native urine samples were used, that were typically held for no more than 4 hours before being analysed, as recommended in the relevant guidelines [5,6] to prevent change in the morphology of the particles. Samples were mixed until homogeneous and then split and run on each measuring procedure as close to the same time as possible. The standardized microscopic urinalysis of native samples (Level 3) was followed by using a KOVA counting chamber. The particle concentration for all particle types was evenly distributed in the evaluated urine samples. Carry-over, precision, diagnostic tests such as sensitivity, specificity, diagnostic accuracy, concordance and one category concordance were investigated according to well-established protocols [7].
Results
No carry-over was detected in any of the samples. UriSed 3 has better precision than microscopy at all of the tested RBC and WBC concentrations. The majority of all coefficients of variation obtained for within series imprecision (CV) using UriSed 3 was 7-16% versus 5,5-67% in case of manual microscopy [8]. Good correlation can be found between UriSed 3 and manual counting chamber for formed elements. The Pearson correlation of quantitative parameters are 0.91 (RBC), 0.93 (WBC). The clinical evaluation of UriSed 3 was based on McNemar test and concordance study. The results are shown in the table above.
Conclusion
UriSed 3 instruments utilize phase-contrast and bright-field microscopy to combine original and innovative technologies whose aim is the progressive improvement of automated urinary sediment examination and the progressive approach to the gold standard manual microscopy method. The automated measurement process of UriSed 3 is reproducible and operator-independent. Those sediment particles that are mostly transparent become visible with phase-contrast microscopy by UriSed 3, which is a spectacular advantage and leads to specific improvement in recognition at several particle types.
References
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3. Zaman Z, Fogazzi GB, Garigali G, Croci MD, Bayer G, Kránicz T. Urine sediment analysis: analytical and diagnostic performances of sediMAX – a new automated microscopy image-based urine sediment analyser. Clin Chim Acta 2010; 411: 147-154.
4. Fogazzi GB, Garigali G. The Urinary Sediment by UriSed Technology. A New Approach to Urinary Sediment Examination. Milano: Elsevier, 2013.
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7. T. Kouri, A. Gyory, R.M. Rowan. ISLH Recommended Reference Procedure for the enumeration of Particles in Urine. Laboratory Hematology 9:58-63, 2003.
8. Haber MH, Galagan K, Blomberg D, Glassy EF, Ward PCJ, editors. Color Atlas of Urinary Sediment; An Illustrated Field Guide Based on Proficiency Testing. Chicago: CAP Press, 2010.
9. Brody L, Webster MC, Kark RM. Identification of elements of urinary sediment with phase-contrast microscopy. JAMA 1968; 206: 1777-1781.
10. Spencer E. and Pedersen Ib. Hand Atlas of the Urinary Sediment. Bright-field, Phase-Contrast, and Polarized Light. Copenhagen: Munksgaard, 1971.
More information on UriSed 3 is available from the manufacturer:
77 Elektronika Kft., Budapest, HUNGARY
Email: sales@e77.hu, web: en.e77.hu
The author
Erzsébet Nagy MD,
Honorary Associate Professor
Head Phisician of Central Laboratory; Hospitaller Brothers of St. John of God Hospital, Budapest
Small tubes, great impact
, /in Featured Articles /by 3wmediaSCIENTIFIC LITERATURE REVIEW: Colorectal cancer
, /in Featured Articles /by 3wmediaHighly sensitive stool DNA testing of Fusobacterium nucleatum as a marker for detection of colorectal tumours in a Japanese population
Suehiro Y, Sakai K, Nishioka M, Hashimoto S, Takami T, Higaki S, Shindo Y, Hazama S, Oka M, Nagano H, Sakaida I, Yamasaki T. Ann Clin Biochem. 2016; pii: 0004563216643970. [Epub ahead of print]
BACKGROUND: Accumulating evidence shows an over-abundance of Fusobacterium nucleatum in colorectal tumour tissues. Although stool DNA testing of Fusobacterium nucleatum might be a potential marker for the detection of colorectal tumours, the difficulty in detecting Fusobacterium nucleatum in stool by conventional methods prevented further explorations. Therefore, we developed a droplet digital polymerase chain reaction (PCR) assay for detecting Fusobacterium nucleatum in stool and investigated its clinical utility in the management of colorectal tumours in a Japanese population.
METHODS: Feces were collected from 60 healthy subjects (control group) and from 11 patients with colorectal non-advanced adenomas (non-advanced adenoma group), 19 patients with colorectal advanced adenoma/carcinoma in situ (advanced adenoma/carcinoma in situ (CIS) group) and 158 patients with colorectal cancer of stages I to IV (colorectal cancer group). Absolute copy numbers of Fusobacterium nucleatum were measured by droplet digital PCR.
RESULTS: The median copy number of Fusobacterium nucleatum was 17.5 in the control group, 311 in the non-advanced adenoma group, 122 in the advanced adenoma/CIS group, and 317 in the colorectal cancer group. In comparison with that in the control group, the Fusobacterium nucleatum level was significantly higher in the non-advanced adenoma group, the advanced adenoma/CIS group and the colorectal cancer group.
CONCLUSIONS: This study illustrates the potential of stool DNA testing of Fusobacterium nucleatum by droplet digital PCR to detect individuals with colorectal tumours in a Japanese population.
A genome-wide assessment of variations of primary colorectal cancer maintained in metastases
Cai Z, Han S, Li Z, He L, Zhou J, Huang W, Xu Y. Gene 2016; 595(1): 18–24.
Colorectal cancer (CRC) is a highly heterogeneous disease that is the third leading cause of cancer-related deaths worldwide. This study presents a genome-wide assessment of variations in primary colorectal cancer maintained in metastases, even in distant metastases. The purpose of this study was to determine whether intratumor heterogeneity is related to disease progression and metastasis in CRC. The results showed that 882 single nucleotide polymorphism (SNP) associated genes and 473 copy number variant (CNV) associated genes specific to metastasis were found. In addition, 57 SNPs mapped to miRNAs showed significant differences between primary tumours and metastases. Functional annotation of metastasis-specific genes suggested that adhesion and immune regulation may be essential in the development of tumours. Moreover, the locus rs12881063 in the fourteenth chromosome was found to have a high rate of the G/C type in metastases. The rate of the G/C type in nearby lymph node metastases was 66.7%, while the rate of the G/C type in distance lymph node metastases was 83.3%. These results indicate that rs12881063 may be the basis for clinical diagnosis of CRC metastasis.
High tumour mast cell density is associated with longer survival of colon cancer patients
Mehdawi L, Osman J, Topi G, Sjölander A. Acta Oncol. 2016; 55(12): 1434–1442.
BACKGROUND: Inflammatory cells and inflammatory mediators play an important role in colorectal cancer (CRC). Previous studies have shown that CRC patients with increased expression of cysteinyl leukotriene receptor 1 (CysLTR1) have a poorer prognosis, and Cysltr1-/- mice display fewer intestinal polyps. However, the role of mast cells (MCs) in colon cancer progression remains unclear. The aim of the present study was to explore the relevance of MCs in CRC.
MATERIAL AND METHODS: A tissue microarray from 72 CRC patients was stained with MC anti-tryptase and -chymase antibodies. Mouse colon tissue was stained with MC anti-tryptase antibody. Immunohistochemistry was used to identify MCs in patients and mice.
RESULTS: Patient colon cancer tissue had in comparison with normal colon tissue a reduced number of MCs, predominantly of chymase-positive cells. Further analysis revealed that patients with a relative high MCD in their cancer tissues showed significantly longer overall survival compared to those with a low MCD [hazard ratio (HR) 0.539; 95% confidence interval (CI), 0.302–0.961]. Similar results were observed in subgroups of patients with either no distant metastasis (p = 0.004), or <75 years (p = 0.015) at time of diagnosis. Multivariate Cox analysis showed that MCD independently correlated with reduced risk of death in colon cancer patients (HR 0.380; 95% CI 0.202-0.713). Additionally, a negative correlation was found between cytoplasmic CysLTR1 expression and number of MCs. In agreement, in the CAC mouse model, Cysltr1-/- mice showed significantly higher MCs in their polyp/tumor areas compared with wild-type mice.
CONCLUSION: A high MCD in cancer tissue correlated with longer patient survival independently from other risk factors for CRC. The concept that MCs have an anti-tumor effect in CRC is further supported by the findings of a negative correlation with CysLTR1 expression in patients and a high MCD in colon polyps/tumors from CysLTR1-/- mice.
Are hemorrhoids associated with false-positive fecal immunochemical test results?
Kim NH, Park JH, Park DI, Sohn CI, Choi K, Jung YS. Yonsei Med J. 2016; 58(1):150–157.
PURPOSE: False-positive (FP) results of fecal immunochemical tests (FITs) conducted in colorectal cancer (CRC) screening could lead to performing unnecessary colonoscopies. Hemorrhoids are a possible cause of FP FIT results; however, studies on this topic are extremely rare. We investigated whether hemorrhoids are associated with FP FIT results.
MATERIALS AND METHODS: A retrospective study was conducted at a university hospital in Korea from June 2013 to May 2015. Of the 34,547 individuals who underwent FITs, 3946 aged ≥50 years who underwent colonoscopies were analysed. Logistic regression analysis was performed to determine factors associated with FP FIT results.
RESULTS: Among 3946 participants, 704 (17.8%) showed positive FIT results and 1303 (33.0%) had hemorrhoids. Of the 704 participants with positive FIT results, 165 had advanced colorectal neoplasia (ACRN) and 539 had no ACRN (FP results). Of the 1303 participants with hemorrhoids, 291 showed FP results, of whom 81 showed FP results because of hemorrhoids only. Participants with hemorrhoids had a higher rate of FP results than those without hemorrhoids (291/1176, 24.7% vs. 248/2361, 10.5%; p<0.001). Additionally, the participants with hemorrhoids as the only abnormality had a higher rate of FP results than those experiencing no such abnormalities (81/531, 15.3% vs. 38/1173, 3.2%; p<0.001). In multivariate analysis, the presence of hemorrhoids was identified as an independent predictor of FP results (adjusted odds ratio, 2.76; 95% confidence interval, 2.24-3.40; p<0.001).
CONCLUSION: Hemorrhoids are significantly associated with FP FIT results. Their presence seemed to be a non-negligible contributor of FP results in FIT-based CRC screening programmes.
Improving obstetric outcome: antenatal thyroid screening
, /in Featured Articles /by 3wmediaMild hypothyroidism, where plasma levels of thyroid stimulating hormone (TSH) are above the ‘normal’ upper limit but where there is no equivalent change in circulating levels of the thyroid hormones tetraiodothyronine (T4) and triiodothyronine (T3), is common in women of childbearing age; the condition is found in up to 3?% of pregnant women. While normally asymptomatic, in pregnant women mild hypothyroidism has been associated with miscarriage, perinatal death and preterm delivery, the major cause of neonatal death. Several studies have investigated whether treatment with levothyroxine, a synthetic thyroid hormone, would improve the obstetric outcome in women with borderline thyroid function, and results from the most recent study were reported at the Society for Endocrinology (BES) conference in November.
In this study, 645 women out of more than 13?000 tested at the end of the first trimester of pregnancy were found to have sub-clinical hypothyroidism (340) or isolated hypothyroxinemia (305). In the latter condition TSH levels are normal but T4 levels are below the lower reference limit. Five hundred and eighteen women with abnormal thyroid function took part in a randomized trial, with half being prescribed levothyroxine and half acting as control. Rates of stillbirth, neonatal death and delivery before 34 weeks were compared, as well as delivery between 34 and 37 weeks and cesarean sections carried out before 37 weeks. It was found that untreated women with abnormal thyroid function had an increased risk of stillbirth, delivery before 37 weeks and having an early cesarean section when compared with women with normal thyroid function and those treated with the synthetic thyroid hormone. Although the authors emphasize that larger trials are needed to confirm their findings, it seems likely that this cheap and safe drug could have a significant impact on obstetric outcome.
In the more developed countries thyroid autoimmunity is the main cause of hypothyroidism, with iodine deficiency being less frequent. Thyroid autoantibodies, particularly thyroid peroxidase antibodies (TPO), can be measurable even in women with biochemically normal thyroid function, and are a risk factor for miscarriage and preterm delivery. Elevated levels are found in up to 20?% of women, but also in as many as 31?% of sub-fertile women. There is a dearth of robust studies to assess the effect of levothyroxine on pregnancy outcomes in these women but it could be that measuring TPO in both sub-fertile as well as pregnant women, followed by treatment with levothyroxine if indicated, could result in many more healthy, full-term babies.
Role of TSH receptor antibodies in the diagnosis of Graves’ disease
, /in Featured Articles /by 3wmediaHyperthyroidism can result from a number of different disorders including Graves’ disease. The diagnostic gold standard is based on radiological tests but measurement of thyroid stimulating hormone receptor antibodies plays an important role in the diagnosis of Graves’. It is important to understand the diagnostic strengths and limitations of these measurements.
by Dr Christopher Boot
Introduction
Hyperthyroidism is relatively common, with a prevalence of between 0.5 and 2 % [1]. A range of symptoms and signs are associated with hyperthyroidism because of the influence of thyroid hormones on multiple organ systems. Many of the most important manifestations are related to effects on the cardiovascular system, which may include tachycardia and arrhythmias. Untreated, hyperthyroidism is associated with significant morbidity and mortality. Hyperthyroidism can usually be diagnosed through the measurement of thyroid stimulating hormone (TSH) and free thyroxine (FT4), with TSH usually suppressed and FT4 raised [occasionally free triiodothyronine (FT3) is raised in the absence of elevated FT4].
The major causes of hyperthyroidism are Graves’ disease and toxic multinodular goitre. Other etiologies include solitary toxic adenoma and thyroiditis (Table 1). Graves’ disease is the most common cause of hyperthyroidism with most other cases due to either toxic multinodular goitre or solitary toxic nodules, which result from autonomous secretion of thyroid hormones (T4 and T3) by one or more nodules. Transient thyrotoxicosis can occur as the result of thyroiditis, secondary to viral infection or autoimmunity.
Graves’ disease is an autoimmune disease characterized by stimulation of the thyroid by TSH receptor stimulating antibodies (TRAbs). This leads to the clinical features typical of hyperthyroidism such as weight loss, heat intolerance, palpitations, anxiety, tremor and tiredness. These autoantibodies may also recognize antigens in other tissues, notably fibroblasts in the eye muscles. This can lead to growth and inflammation of fat cells and muscles around the eye leading to Graves’ orbitopathy, characterized by upper eyelid retraction, lid lag, swelling, conjunctivitis and exophthalmos.
It is important to differentiate between Graves’ disease and other causes of hyperthyroidism as the approach to treatment may depend on etiology. Current guidelines recommend that all cases of hyperthyroidism are referred to an endocrinologist for further investigation to determine the cause and a treatment plan [2, 3]. This article focuses on the role of TRAb measurements in the diagnosis of Graves’ although TRAbs also provide prognostic information [4] and have a role in assessing the risk of neonatal hyperthyroidism in pregnancies involving maternal Graves’ [5].
Diagnosis of Graves’ disease
Determining the underlying cause of hyperthyroidism relies on a combination of clinical history, physical examination, biochemical testing and imaging. Certain findings are highly suggestive of Graves’ disease such as a symmetrically enlarged, non-nodular thyroid and evidence of orbitopathy. The most commonly used imaging tests are radiolabel uptake scans, which allow visualization of a thyroid radiolabel uptake pattern. In Graves’ disease there is homogenous, increased uptake of label across the thyroid, whereas in multinodular goitre there is patchy uptake with increased uptake at the sites of the over-active nodules. Radioactive iodine has largely been replaced with technetium pertechnetate (99mTc), which mimics the behaviour of iodine but exposes patients to lower radiation doses. The recommended role for TRAbs in the diagnosis of Graves’ varies. One recommended approach is to measure TRAbs in new cases of primary hyperthyroidism and where TRAb results are positive to diagnose Graves’ disease (Fig. 1). Where TRAb results are negative, uptake scans can then be used to distinguish Graves’, toxic nodule(s) and thyroiditis [6]. However, some guidelines have recommended an uptake scan as the first-line test, with TRAbs only used in certain situations [7].
TRAb assays
There are two main categories of TRAb assays. The majority of assays in clinical use detect TRAbs in patient samples through their competition with an added TSH receptor ligand for binding of the TSH receptor. These competition-based assays are sometimes referred to as thyrotropin-binding inhibitory immunoglobulin (TBII) assays. Competition-based assays do not discriminate between stimulatory TRAbs (as found in Graves’) or non-stimulating (inhibiting or neutral) TRAbs. In cases of hyperthyroidism it is assumed that any detected TRAbs are stimulating. The second category of TRAb assay is bioassays, which detect only stimulating TRAbs.
Competition-based assays have evolved over the years. Early assays used porcine thyroid membrane extracts and detected the inhibition of binding of radiolabelled TSH to these extracts. Liquid-phase assays were developed when recombinant human TSH receptor became available and the inhibition of radiolabelled TSH to recombinant TSH receptor was detected. Further evolution of competition assays involved replacement of labelled TSH with monoclonal anti-TSH receptor antibodies as the competing ligand. Modern TRAb assays typically use fluorescent or chemiluminescent labels and can be automated allowing high throughput.
Bioassays for stimulating TRAbs detect the production of cAMP in cells incubated with patient serum. Current bioassays use Chinese hamster ovary (CHO) cells transfected with human TSH receptor. These cells produce cAMP in response to TSH receptor stimulation. cAMP can be measured by immunoassay or a luciferase reporter gene may be used to generate a chemiluminescent signal in response to increasing cAMP. TRAb bioassays are more complex and expensive than competition-based assays and less commonly used in clinical practice.
Diagnostic performance of TRAb assays
The current generation of competition-based TRAb assays are generally reported to offer a high degree of diagnostic specificity and sensitivity for Graves’ disease. A meta-analysis of clinical studies using current assays indicated a pooled specificity of 99 % and sensitivity of 97 % [8]. This high diagnostic performance has led some authors to recommend TRAbs as a first-line test to distinguish Graves’ disease from other causes of hyperthyroidism. This may lead to a quicker and more cost effective diagnosis in many cases compared to initial use of imaging tests [9]. In particular, the high diagnostic specificity achieved means that untreated, hyperthyroid patients with positive TRAbs are highly likely to have Graves’ disease so that uptake scans may not be necessary in this scenario, particularly when the clinical presentation suggests Graves’. However, a recent study that compared the diagnostic sensitivity of a number of competition-based TRAb assays found significant variability with sensitivity varying from 65 to 100 % depending on the TRAb assay used [10]. Therefore, a negative TRAb result may not always rule out Graves’ disease with a high degree of certainty.
Assessment of the diagnostic performance of TRAbs in a UK tertiary referral centre
In view of the variability in reported diagnostic sensitivity and the identification of a number of cases of apparent TRAb-negative Graves’ disease in our centre, a retrospective study of the performance of TRAbs in the diagnosis of Graves’ was carried out. The Kryptor (ThermoFisher) TRAb assay was used throughout the period of the study. Results from all TRAb requests for patients referred with a new presentation of thyrotoxicosis were gathered over 18 months. Routine diagnosis of the etiology of hyperthyroidism was based on the uptake pattern on 99mTc scintigraphy, clinical course and other features in addition to TRAb concentrations. Ninety-nine cases of Grave’s disease were identified and 131 cases where an alternative cause of thyrotoxicosis was diagnosed. There was some overlap in TRAb concentrations between patients with Graves’ and patients with other etiologies (Fig. 2). Using the diagnostic cut-off of >1.8 IU/L suggested by the manufacturers of the assay, diagnostic sensitivity was 81.8 % (18 of 99 cases of Grave’s were TRAb-negative), whereas diagnostic specificity was 99.2 %. Applying a lower cut-off of >1.2 IU/L resulted in an improved sensitivity of 88.9 % but slightly lower specificity of 97.7 %.
This data from our centre demonstrated a significant number of cases of TRAb-negative Graves’ disease among patients referred with a new presentation of thyrotoxicosis. The diagnostic sensitivity of the Kryptor TRAb assay, therefore, appears to be lower than that suggested by the manufacturer’s data (96.3 %). This could possibly be as a result of more stringent classification of Graves’ in other studies, whereas this data represents the range of patients investigated in practice, which includes cases of borderline/mild hyperthyroidism. Of the 99 cases of Graves’ disease in this study, 40 patients had a FT4 of less than 30 pmol/L. Twenty percent of patients in this group had a TRAb level of <1.0 IU/L (the lower limit of quantification for the assay). Of the remaining 59 cases of Graves’ disease with a FT4 of ≥30 pmol/L, only 5 % had a TRAb level of < 1.0 IU/L. This suggests that cases of Graves’ with milder biochemical thyrotoxicosis on presentation are more likely to be TRAb-negative. Applying a lower diagnostic cut-off than that recommended by the manufacturer may improve the sensitivity of the Kryptor TRAb assay in the diagnosis of Grave’s disease. Practice in our laboratory is now to report an ‘equivocal’ range of 1.0–1.8 IU/L in addition to a cut-off for positivity of >1.8 IU/L. This better reflects the overlap in TRAb concentrations between Graves’ and other causes of thyrotoxicosis observed in our study than a binary positive/negative threshold. However, no cut-off provided 100 % diagnostic sensitivity for Graves’ disease.
Summary
TRAb assays are useful in the differentiation of Graves’ disease from other causes of thyrotoxicosis. In particular, TRAbs appear to provide a high degree of diagnostic specificity so that hyperthyroid patients with positive TRAb results are highly likely to have Graves’. Radioactive uptake scans may, therefore, not be necessary in all cases of TRAb-positive hyperthyroidism. However, some studies (including our local data) suggest that the diagnostic sensitivity of a negative TRAb result alone is not sufficient to reliably rule out Graves’ disease. Diagnostic performance is likely to vary between TRAb assays, so assay-specific reference data should be used for interpretation.
References
1. Vanderpump MPJ. The epidemiology of thyroid disease. Br Med Bull. 2011; 99: 39–51.
2. Ross DS, Burch HB, Cooper DS, Greenlee MC, Laurberg P, Maia AL, Rivkees SA, Samuels M, Sosa JA, et al. 2016 American Thyroid Association guidelines for diagnosis and management of hyperthyroidism and other causes of thyrotoxicosis. Thyroid 2016; 26: 1343–1421.
3. UK Guidelines for the use of thyroid function tests. Association of Clinical Biochemistry, British Thyroid Association and British Thyroid Foundation 2006.
4. Vos XG, Endert E, Zwinderman AH, Tijssen JG, Wiersinga WM. Predicting the risk of recurrence before the start of antithyroid drug therapy in patients with Graves’ hyperthyroidism. J Clin Endocrinol Metab. 2016; 101(4):1381–1389.
5. Laurberg P, Nygaard B, Glinoer D, Grussendorf M, Orgiazzi J. Guidelines for TSH-receptor antibody measurements in pregnancy: results of an evidence-based symposium organized by the European Thyroid Association. Eur J Endocrinol. 1998; 139: 584–586.
6. Vaidya B, Pearce SHS. Diagnosis and management of thyrotoxicosis. BMJ 2014; 349: g5128.
7. Bahn RS, Burch HB, Cooper DS, Garber JR, Greenlee MC, Klein I, Laurberg P, McDougall IR, Montori VM, et al. Hyperthyroidism and other causes of thyrotoxicosis: management guidelines of the American Thyroid Association of Clinical Endocrinologists. Endocr Pract 2011; 17: 457–520.
8. Tozzoli R, Bagnasco M, Giavarina D, Bizzaro N. TSH receptor autoantibody immunoassay in patients with Graves’ disease: improvement of diagnostic accuracy over different generations of methods. Systematic review and meta-analysis. Autoimmun Rev. 2012; 12: 107–113.
9. McKee A, Peryerl F. TSI assay utilization: impact on costs of Graves’ hyperthyroidism diagnosis. Am J Manag Care 2012; 18: e1–14.
10. Diana T, Wüster C, Kanitz M, Kahaly GJ. Highly variable sensitivity of five binding and two bio-assays for TSH-receptor antibodies. J Endocrinol Invest. 2016; 39: 1159–1165.
The author
Christopher Boot PhD, FRCPath
Department of Blood Sciences, Royal
Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust,
Newcastle upon Tyne, UK
*Corresponding author
E-mail: christopher.boot@nuth.nhs.uk
Chromogranin A as a biomarker for the detection of neuroendocrine tumours
, /in Featured Articles /by 3wmediaNeuroendocrine tumours (NETs) are a heterogeneous group of tumours that vary depending on their anatomical sites, functionality and hormones produced. They are often silent clinically, and diagnosis is usually delayed. Chromogranin A (CgA) is the best-known general biomarker which is used for the diagnosis and management of NETs. It can be measured in serum or plasma using different analytical methods that include RIA, IRMA or ELISA. Raised circulating CgA is considered to be a relatively sensitive marker for the diagnosis of NET. As the test is rather non-specific, the diagnostic yield can be improved if other non-NET related conditions with raised CgA including renal failure, cardiac, hepatic and inflammatory diseases and use of proton pump inhibitor (PPI) are excluded.
by Dr Elham AlRisi and Prof. Waad-Allah S. Mula-Abed
Introduction
Neuroendocrine tumours (NETs) are a group of tumours that are usually derived from the cells of the nervous and endocrine systems. The tumours are characterized by being rare, heterogeneous and may affect different tissues and organs with neuroendocrine elements including the gastroenteropancreatic system, lungs, thyroid, parathyroid, pituitary, sympathoadrenals, and other tissues [1]. The NETs are distinctive in that their structural components of cells have the ability to synthesize, store, and secret bioactive amines and peptide hormones, a phenomenon termed ‘amine precursor uptake and decarboxylation’ (APUD) [2]. Although NETs may be considered rare, there is, however, increasing interest in their diagnosis, reported incidence and increased survival duration over time, suggesting that NETs are more prevalent than were previously reported.
The US Surveillance, Epidemiology, and End Results (SEER) Program registries in their search from 1973 to 2004, identified 35 618 patients with NETs with a significant increase in the reported annual age-adjusted incidence of NETs from 1973 (1.09/100 000) to 2004 (5.25/100 000). Using the SEER registry data, the estimated 29-year limited-duration prevalence of NETs in January 2004, was found to be 9263 and the estimated 29-year limited-duration prevalence in the United States on that date was 103 312 cases (35/100 000) [3]. The clinical presentations in patients with NETs vary according to the site where the tumour develops, which can be anywhere in the body and can range from a silent tumour, to one that is associated with an overproduction of the hormone/peptide (with their pathophysiological and clinical sequels) known to be produced by that tissue, or to a metastatic tumour. The growing interest in NETs in recent years is attributed to the increasing medical awareness, availability of laboratory markers for the detection of NETs particularly the chromogranins and the wide use of radiological imaging that have increased the diagnostic yields of these tumours.
Physiology of the granin family including chromogranin A
The secretory granules of the neuroendocrine and endocrine cells contain a family of highly acidic proteins, the granins. The most abundant forms of granins are chromogranin A (CgA), chromogranin B (CgB), secretogranin II (SgII), whereas granins the other forms that include SgIII, VGF, 7B2, and proSAAS are much less distributed in these granules. The granins are involved in the granulogenesis of the secretory granule biogenesis, with some being processed to form numerous peptides that have different physiological activities. CgA, the most studied chromogranin, was first isolated from the chromaffin cells of the adrenal medulla. It is a single polypeptide chain of 439 amino acids and 10 dibasic cleavage sites; the CgA gene is localized on chromosome 14q32 [4, 5].
Chromogranins contribute intracellularly to the overall vesicle biogenesis and facilitate the processing and regulation of other secretory proteins. Processing of chromogranins gives rise to multiple bioactive peptides that include the vasodilator vasostatin (human CgA 1–76), catecholamine release inhibitor catestatin (human CgA 352–372) and dysglycemic peptide pancreastatin (human CgA 250–301) [6]. Pancreastatin regulates glucose metabolism in cells and certain organs by inhibiting glucose-mediated insulin release from pancreatic islet cells, and inhibiting glucose uptake by adipocytes and hepatocytes. Other contributing functions of CgA include its involvement in regulating endothelial barrier, tumour angiogenesis, anti-apoptosis, and vascular structure and permeability [7].
Laboratory methods for the measurement of chromogranin A
There are different approaches for the determination of circulating CgA. The currently available methods include radioimmunoassay (RIA), immunoradiometric assay (IRMA) and enzyme-linked immunosorbent assay (ELISA). The introduction of commercially available ELISA kits for CgA assay (with their advantages of having long shelf life, technical ease, safety of use, and reported reasonable validity) has greatly improved the measurement of CgA in the diagnosis and clinical management of patients with of NETS. Currently there is increasing availability of these kits for measuring CgA in many hospital laboratories.
CgA can be measured using plasma or serum specimens. Although plasma CgA has been reported in a few studies to be higher than in serum, the difference may not affect clinical interpretation, particularly if there is consistent use of a single specimen type [6]. Different results might be reported by the different techniques, which might affect the validity indicators using these techniques. There are no universal standards for the techniques used and no universally accepted technique. There are reports that favour RIA over other methods; however, the practical advantages of ELISA techniques, especially the long shelf life, might make them attractive methods for use by many laboratories and might explain their widespread use in today’s practice [8]. Nevertheless, the selection of the analytical method to be used depends on the technical feasibility and convenience in the laboratory.
Chromogranin A and neuroendocrine tumours
CgA and its fragments are usually present in the circulation in equimolar concentration with the secretory activity of the secreting neuroendocrine tissue of both normal subjects and patients with different NETs; hence, CgA concentration in the circulation can be measured to provide information on the diagnosis, prognosis and monitoring of patients with these tumours, if other non-NET related physiological, pathological and pharmacological causes are excluded.
CgA is usually secreted by a variety of NETs, which include: carcinoids, pheochromocytoma, paraganglioma, medullary carcinoma of thyroid, parathyroid adenomas, pulmonary NETs including small cell lung cancer, gastroenteropancreatic (GEP-NETs) including functioning and nonfunctioning pancreatic islet cell tumours, some pituitary adenomas and other APUD tumours. The highest CgA values are observed in small intestine NETs and GEP-NETs associated with MEN1. Moderate-to-high CgA values are noted in pancreatic NETs, Zollinger-Ellison syndrome and gastrinomas. CgA is more frequently elevated in well-differentiated tumours compared to poorly differentiated NETs [9]. Different clinical validity indicators for CgA have been reported by different workers in the different patient cohorts. Yang et al. through their search of 13 studies that included 1260 patients with NETs and 967 healthy controls, reported an overall sensitivity, specificity and diagnostic odds ratio (DOR) of 0.73, 0.95 and 56.3, respectively, while the summary positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 14.56 and 0.26, respectively [10]. In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.896. The pooled sensitivity and specificity values of CgA were 0.73 and 0.95, respectively, whereas the pooled PLR and NLR values were 14.56 and 0.26, respectively for the diagnosis of NETs. All these data suggested a higher diagnostic accuracy of CgA for the diagnosis of NETs. Among the included studies, three different assays were used to measure the circulating CgA, the sensitivity was both 0.74 by ELISA and RIA assays, and 0.69 by IRMA assay. The specificity was 0.93, 0.95 and 1.00 for ELISA, RIA and IRMA assays, respectively.
CgA values also have a prognostic role, as their high levels correlate with poor prognosis and short survival in certain NETS [11]. This relationship is usually limited in patients with gastrinomas, who have high CgA values despite the small primary tumour size and absence of metastases, possibly due to CgA secretion from G cells. Also, CgA values reflect the tumour burden, and monitoring the disease by CgA usually helps in detecting tumour recurrence or progression following treatment by surgery or radiotherapy. In patients with midgut NET, serum CgA level was the first marker to reflect tumour recurrence compared with urinary 5HIAA and radiological measurements [12]. Also, in pheochromocytoma, especially when large and lacking the proper hormonal characterization, CgA may be the only laboratory guide in the diagnosis and management of patients with such tumours [13].
Pitfalls in the interpretation of chromogranin A values
Although CgA is a useful general marker for the diagnosis and management of NETs, its universal secretion by almost all neuroendocrine cells makes its use confounded by its co-elevation in a variety of non-NET conditions including non-NET malignancies [14–16]. Hence, interpretation of CgA results must be done in the context of the overall confounding factors, whether physiological, pharmacological or pathological. Such conditions include the use of proton pump inhibitors (PPIs) or H2-receptor blockers, chronic atrophic gastritis, impaired renal function, cardiac failure, hepatic insufficiency, inflammatory bowel disease, benign prostatic hypertrophy or malignancy, rheumatoid arthritis, untreated essential hypertension, and some non-NET neoplasms. The pattern of elevation in serum CgA in certain non-NET conditions has been suggested recently to be utilized as a biomarker and prognostic marker in the stratification of some chronic diseases. This is particularly the case for heart failure where CgA might have a role in identifying those at higher risk of short- or long-term mortality [17]. The role of CgA in diabetes is not clear. However, CgA and its cleavage fragments, including WE-14, might play a part in the pathogenesis of type 1 diabetes mellitus, possibly as a T-cell autoantigen in pancreatic β-cell destruction [18]. Therefore, CgA might have a potential use as a biomarker in the future [18].
Conclusion
Chromogranin A is a secretory protein of neuroendocrine origin that is usually present with its fragments in the circulation as a result of the secretory activity of the secreting neuroendocrine cells of both normal subjects and patients with different NETs. It is the best-known general biomarker which is increasingly used for the diagnosis and management of NETs. It can be measured in plasma or serum using different analytical methods that include RIA, IRMA or ELISA. Raised circulating CgA is considered to be a relatively sensitive marker for the diagnosis of NET particularly if there is clinical suspicion and other work-up investigations that are in plan. Its measurement is also of value in monitoring the progress of treatment and prognosis of the disease. The diagnostic yield is improved if other non-NET related diseases or conditions are considered and excluded prior to the interpretation of CgA values. These conditions include the use of PPIs or H2-receptor blockers, chronic atrophic gastritis, impaired renal, cardiac, or hepatic insufficiency, inflammatory bowel disease, rheumatoid arthritis, and some non-NET neoplasms.
References
1. Kaltsas GA, Besser GM, Grossman AB. The diagnosis and medical management of advanced neuroendocrine tumors. Endocr Rev. 2004; 25(3): 458–511.
2. Pearse AG. Common cytochemical and ultrastructural characteristics of cells producing polypeptide hormones (the APUD series) and their relevance to thyroid and ultimobranchial C cells and calcitonin. Proc R Soc Lond B Biol Sci. 1968; 170(1018): 71–80.
3. Yao JC, Hassan M, Phan A, Dagohoy C, Leary C, Mares JE, Abdalla EK, Fleming JB, Vauthey JN, Rashid A, Evans DB. One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol. 2008; 26(18): 3063–3072.
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The authors
Elham AlRisi MD; Waad-Allah S. Mula-Abed* MBChB MSc FRCPath
Directorate of Laboratory Medicine and Pathology, Royal Hospital, Muscat, Oman
*Corresponding author
E-mail: drsharef@live.com
The potential of the microbiome for colorectal cancer screening
, /in Featured Articles /by 3wmediaAlterations of the microbiome are associated with colorectal cancer. Research suggests that microbiome data could improve colorectal cancer screening. Analysis of the microbiome directly from existing screening methods offers the opportunity to rapidly translate this research into practice, with the potential to develop a multifactorial colorectal cancer screening tool.
by Dr Caroline Young and Professor Philip Quirke
Current colorectal cancer screening methods
Different countries have adopted various approaches to colorectal cancer screening. They share a common goal: detection of asymptomatic adenomas or early stage carcinomas, as detection and treatment at an earlier stage is associated with improved survival [1]. Two main screening methods are in use: detection of fecal occult blood and visualization of the colon. Stool DNA testing has recently been approved but is currently prohibitively expensive.
Detection of fecal occult blood can be achieved using the guaiac fecal occult blood test (gFOBT) or an immunochemical method, fecal immunochemical test (FIT). The gFOBT method requires participants to apply stool to a gFOBT card on three occasions and return this to a screening centre through the post. Hydrogen peroxide is applied and if heme is present, blue discolouration occurs. This method has been shown to reduce mortality by 16 % [2]. The FIT method requires participants to insert a FIT probe into stool and return this to a screening centre through the post. An antibody-based assay is used to detect globin. FIT is more sensitive and specific, can be analysed quantitatively and has improved acceptability [3]. Participants in whom fecal occult blood is detected above a threshold, by either method, are referred for colonoscopy.
Alternatively, direct visualization of the colon by colonoscopy/sigmoidoscopy can be undertaken as first-line screening. Limitations include procedural risks, associated costs, workforce capacity and reduced acceptability [4].
The microbiome and colorectal cancer
The microbiome can be characterized using a number of technologies: next generation sequencing (NGS) of bacterial 16SrRNA, whole genome shotgun metagenomics of bacterial communities or the analysis of fecal metabolites (metabolomics). These techniques have enabled an appreciation of the diversity and function of the microbiome in health and disease.
Epidemiological studies demonstrate that the incidence of colorectal cancer is highest in countries with a Western culture, which encompasses Western diet, sanitation and hygiene, medication use, urbanization, etc. [5]. Migrant populations to such countries acquire the increased risk, suggesting an environmental risk factor. African Americans, who typically have a high incidence of colorectal cancer, have been shown to have different microbiomes to Native Africans, who have a low incidence of colorectal cancer [6] and the diets typical of these two groups have been shown to differentially influence the microbiome [7].
Numerous studies have found differences in the microbiome, ‘dysbiosis’, of patients with colorectal adenomas or carcinomas compared to healthy controls [8]. In general, dysbiosis is characterized by a decrease of short chain fatty acid-producing bacteria, an increase of bacteria that produce bile salts or hydrogen sulphide, an increase of pathogenic bacteria and inflammation [9]. In particular, the species Fusobacterium nucleatum, a Gram-negative oral commensal, has been associated with colorectal carcinoma in many studies.
Animal models have explored potential mechanisms [10] and interestingly show that risk is transferable with transplant of dysbiotic microbiomes. This suggests that dysbiosis may be causative or promotional of the development of colorectal cancer, rather than merely associative.
Given the association between dysbiosis and colorectal cancer, researchers have considered whether the microbiome could be used as a screening tool.
The microbiome compared to gFOBT
Several studies have compared the accuracy of the microbiome as a screening tool to gFOBT. Amiot et al. showed that a screening model combining age plus microbiome (typed by qPCR) was no better than a model combining age plus gFOBT [11]. However, metabolomic analysis [by 1(H)-NMR spectroscopy] was more accurate than gFOBT [12]. Zeller et al. created a screening model that combined metagenomic data with gFOBT results, which lead to an increase in sensitivity compared to gFOBT alone. This model was subsequently validated in a cohort of a different nationality. It showed some ability to distinguish colorectal cancer from a distinct bowel condition (inflammatory bowel disease) and could be extrapolated to NGS of 16SrRNA (a cheaper method) [13].
Zackular et al. used 16SrRNA analysis of the microbiome to create models combining microbiome data and patient metadata that were more accurate than models based on metadata alone [14]. A model comprising BMI, microbiome data and gFOBT was more accurate at distinguishing adenoma from carcinoma than gFOBT alone. Yu et al. used metagenomics to identify two discriminatory bacterial genes that they then validated as biomarkers by qPCR (a cheaper method) in a cohort of a different nationality. The area under the receiver operating characteristic (ROC) curve for discriminating carcinoma from controls was 0.84, although gFOBT or FIT screening was not performed for comparison [15].
The microbiome compared to FIT
As FIT is replacing gFOBT in many screening programmes and has a higher sensitivity, comparing the accuracy of the microbiome as a screening tool with FIT is more appropriate.
Baxter et al. used 16SrRNA to create a screening model that combined microbiome data and FIT to discriminate healthy controls from cases with either adenoma or carcinoma [16]. This model was more sensitive but less specific than FIT alone; it detected 70% of cancers and 37% of adenomas which were missed by FIT. Liang et al. [17] identified four bacterial species (one being F. nucleatum) by qPCR that could distinguish colorectal carcinoma from healthy controls with greater accuracy than FIT. Combining microbiome and FIT data afforded greater accuracy still.
Goedert et al. [18] analysed the microbiome by 16SrRNA in patients with a positive FIT result at baseline. The microbiome data gave an area under the ROC curve for discriminating between healthy controls and colorectal adenoma of 0.767.
Limitations of current research
The studies mentioned above show promise for the microbiome as a potential colorectal cancer screening tool. However, they should be interpreted with a degree of caution, owing to a number of limitations which mean that aspects of the studies do not realistically reflect screening conditions. Several of the studies assessed participants at increased risk of colorectal cancer or who were symptomatic. Some collected stool samples following bowel preparation and colonoscopy; one study found that this did not affect the significance of results [16], whereas another found that it did [15]. Several studies included adenomas <10 mm within their control groups. Many of the studies created models that distinguished adenomas from carcinomas or carcinomas from healthy controls; few designed models to discriminate between healthy controls and participants with any colorectal lesion (i.e. either adenoma or carcinoma).
All of the studies used whole stool samples that were refrigerated or frozen by participants at home or delivered within a limited time window to research centres. This method of sample collection would not translate to national screening programmes, which already struggle with poor participant uptake. In light of this, researchers have, therefore, investigated whether the microbiome can be analysed directly from the existing screening tools, gFOBT or FIT.
Analysing the microbiome directly from existing screening tools
Sinha et al. emphasize the need to assess reproducibility, stability over time and how accurately results reflect the gold standard (fresh or immediately frozen stool) when analysing different methods of microbiome sample collection [19]. They found that 16SrRNA microbiome results were similar when analysed from unprocessed or processed gFOBT cards and, in addition to Dominianni et al. [20], showed stability after storage at room temperature for several days. This work was extended by Taylor et al. [21] who demonstrated that the microbiome is stable when analysed by 16SrRNA from processed gFOBT cards stored at room temperature for up to 3 years.
Lotfield et al. showed that metabolomic assessment of the microbiome by ultra-performance liquid chromatography and high resolution/tandem mass spectrometry was stable and accurate (albeit with a degree of bias affecting certain metabolite groups) when analysed directly from gFOBT samples but not from FIT samples [22]. This suggests that different methods of sample collection may be more or less appropriate dependent upon the method of microbiome analysis.
These studies have assessed methods of microbiome sample collection from healthy volunteers. Baxter et al. [23] have analysed the microbiome directly from processed FIT from subjects with normal bowels, colorectal adenomas or carcinomas. Their study comes with the caveat that some of the stool samples were collected after bowel preparation and colonoscopy; samples were stored at −80 °C before being thawed and transferred to FIT; FIT was refrigerated for up to 2 days, processed, then stored at −20 °C before being thawed for microbiome analysis. The study demonstrated that a screening model to discriminate between healthy controls and subjects with any colonic lesion had a similar area under the ROC curve whether microbiome analysis was performed directly from FIT samples or whole stool samples.
As an alternative to stool, Westenbrink et al. analysed microbiome-related volatile organic compounds from urine [24] and described a similar sensitivity for the detection of colorectal cancer as gFOBT or FIT.
Conclusion
Research suggests that there is potential for microbiome analysis to both augment and to be integrated with existing screening methods. The landscape of colorectal cancer screening is changing [25]; it seems likely that a more sophisticated, multifactorial screening tool will be adopted. Microbiome analysis is likely to contribute and may even offer information beyond that of screening, e.g. prevention or treatment targets [26]. Furthermore, collection of longitudinal, population-based microbiome data via national screening programmes will transform the field of microbiome research.
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The authors
Caroline Young* MA, BMBCh; Philip Quirke BM, PhD, FRCPath, FMedSci
Wellcome Trust Brenner Building, St James University Hospital, Leeds LS9 7TF, UK
*Corresponding author
E-mail: caroline.young4@nhs.net