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
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.
4. Banks P, Helle K. The release of protein from the stimulated adrenal medulla. Biochem J 1965; 97(3): 40C–41C.
5. Bartolomucci A, Possenti R, Mahata SK, Fischer-Colbrie R, Loh YP, Salton SR. The extended granin family: structure, function, and biomedical implications. Endocr Rev. 2011; 32(6): 755–797.
6. Bech PR, Martin NM, Ramachandran R, Bloom SR. The biochemical utility of chromogranin A, chromogranin B and cocaine- and amphetamine-regulated transcript for neuroendocrine neoplasia. Ann Clin Biochem. 2014; 51(1): 8–21.
7. Taupenot L, Harper KL, O’Connor DT. The chromogranin-secretogranin family. N Engl J Med. 2003; 348(12): 1134–1149.
8. Stridsberg M, Eriksson B, Oberg K, Janson ET. A comparison between three commercial kits for chromogranin a measurements. J Endocrinol. 2003; 177(2): 337–341.
9. Modlin IM, Gustafsson BI, Moss SF, Pavel M, Tsolakis AV, Kidd M. Chromogranin A- biological function and clinical utility in neuro endocrine tumor disease. Ann Surg Oncol. 2010; 17(9): 2427–2443.
10. Yang X, Yang Y, Li Z, Cheng C, Yang T, Wang C, Liu L, Liu S. Diagnostic value of circulating chromogranin a for neuroendocrine tumors: a systematic review and meta-analysis. PLoS One 2015; 10(4): e0124884.
11. Ekeblad S, Skogseid B, Dunder K, Oberg K, Eriksson B. Prognostic factors and survival in 324 patients with pancreatic endocrine tumours treated at a single institution. Clin Cancer Res. 2008; 14(23): 7789–7803.
12. Welin S, Strisberg M, Cunningham J, Granberg D, Skogseid B, Oberg K, Eriksson B, Janson ET. Elevated plasma chromogranin A is the first indication of recurrence in radically operated midgut carcinoid tumors. Neuroendocrinology 2009; 89(3): 302–307.
13. Mula-Abed WA, Ahmed R, Ramadhan FA, Al-Kindi MK, Al-Busaidi NB, Al-Muslahi HN, Al-Lamki MA. A rare case of adrenal pheochromocytoma with unusual clinical and biochemical presentation: A case report and literature review. Oman Med J. 2015; 30(5): 382–390.
14. Gut P, Czarnywojtek A, Fischbach J, Bączyk M, Ziemnicka K, Wrotkowska E, Gryczyńska M, Ruchała M. Chromogranin A – unspecific neuroendocrine marker. Clinical utility and potential diagnostic pitfalls. Arch Med Sci. 2016; 12(1): 1–9.
15. Glinicki P, Jeske W. Chromogranin A (CgA) – the influence of various factors in vivo and in vitro, and existing disorders on its concentration in blood. Endokrynol Pol. 2011; 62(Suppl 1): 25–28 (in Polish).
16. Capellino S, Lowin T, Angele P, Falk W, Grifka J, Straub RH. Increased chromogranin A levels indicate sympathetic hyperactivity in patients with rheumatoid arthritis and systemic lupus erythematosus. J Rheumatol. 2008; 35(1): 91–99.
17. Goetze JP, Hilsted LM, Rehfeld JF, Alehagen U. Plasma chromogranin A is a marker of death in elderly patients presenting with symptoms of heart failure. Endocr Connect. 2014; 3(1): 47–56.
18. Stadinski BD, Delong T, Reisdorph N, Reisdorph R, Powell RL, Armstrong M, Piganelli JD, Barbour G, Bradley B, Crawford F, Marrack P, Mahata SK, Kappler JW, Haskins K. Chromogranin A is an autoantigen in type 1 diabetes. Nat Immunol. 2010; 11(3): 225–231.
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.
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
Molecular differentiators of uterine leiomyosarcoma and endometrial stromal sarcoma
, /in Featured Articles /by 3wmediaLeiomyosarcoma and endometrial stromal sarcoma are the most common types of uterine sarcoma, a group of rare and clinically aggressive mesenchymal cancers. These two sarcomas may have overlapping clinical presentation, morphology and protein expression profiles, making their diagnosis occasionally difficult. This article discusses molecular approaches that may be applied to the diagnosis of these two cancers and may generate data expanding our therapeutic options and patient outcome.
by Professor Ben Davidson
Introduction
The majority of cancers affecting the uterine corpus are carcinomas, i.e. tumours of epithelial origin. Uterine sarcomas, tumours that are of mesenchymal origin, are a group of rare and clinically aggressive tumours constituting 7 % of all soft tissue sarcomas and 3 % of malignant uterine tumours [1, 2]. The most common entities within this group are leiomyosarcoma (LMS) and endometrial stromal sarcoma (ESS) [2, 3]. Although LMS and ESS are readily diagnosed based on morphology and a limited immunohistochemistry (IHC) panel in many cases, some tumours may pose diagnostic difficulty, and currently used antibodies are not 100 % sensitive or specific [4]. Improved understanding of the molecular make-up of these tumours may lead to more accurate diagnosis and better understanding of their biology, eventually improving our ability to design targeted therapy approaches with the objective of improving patient outcome.
The genetic make-up of ESS and LMS
Low-grade ESS, the more common type of ESS, is characterized by several gene rearrangements creating fusion genes, of which the first described was fusion of the zinc finger gene 1 JAZF1, located at 7p15, and JJAZ1, also termed SUZ12, at 17q21 through a 7;17-translocation. Other fusions in low-grade ESS include the one between JAZF1 and the PHD finger protein 1 gene (PHF1) in 6p21, as well as between PHF1 and enhancer of polycomb homologue 1 (EPC1) gene at 10p11 and the MYST/Esa1 associated factor 6 gene (MEAF6) at 1p34. X chromosome rearrangements include fusion of the open reading frame CXorf67 and the BCL-6 interacting corepressor (BCOR) gene, both at Xp11, with the MBT domain-containing protein 1 gene (MBTD1) at 17q21 and with the zinc finger CCCH-type containing 7B gene (ZC3H7B) at 22q13, respectively.
High-grade ESS is characterized by a fusion between the tyrosine 3/tryptophan 5 monooxygenase gene (YWHAE) gene at 17p13 and the NUT family member gene (NUTM2; previously known as FAM22) at 10q22, creating YWHAE-NUTM fusion through a 10;17-translocation (reviewed by Davidson and Micci, invited review submitted to Expert Rev Mol Diagn). These alterations were recently confirmed by analysis of the ESS transcriptome and/or whole-exome sequencing, including the application of next generation sequencing [5–7].
The body of data with respect to the molecular characteristics of LMS is more limited. An observation found in several studies is the presence of exon 2 mutations in the mediator complex subunit 12 (MED12) gene on chromosome band Xq13.1 in some LMS. MED12 protein forms complex with MED13, cyclin-dependent kinase 8 (CDK8), and cyclin C, termed the CDK8 submodule of the Mediator, the mediator being a large multiprotein complex regulating transcription [8]. Though less frequent in LMS compared to leiomyomas, the benign counterpart of LMS, this finding appears to be absent in other malignant soft tissue sarcomas, and is rare in carcinomas, and is thus potentially relevant in the diagnostic setting (reviewed by Croce & Chibon [9]).
RNA sequencing of 99 LMS, of which 49 were uterine, identified 3 distinct molecular subtypes. Leiomodin (LMOD1) and ADP-ribosylation factor-like 4C (ARL4C) were found to be markers for type I and II tumours, respectively, and the latter group was associated with poor prognosis when located in the uterus [10].
Comparative molecular analysis of ESS and LMS
Our group performed two studies of uterine LMS and ESS with the aim of identifying novel biomarkers that may expand the arsenal of markers currently used in diagnosing these tumours, as well as improving our understanding of their unique biology.
In the first study, the gene expression profiles of 7 ESS and 13 LMS were compared using the HumanRef-8 BeadChip from Illumina. We identified 549 unique probes that were significantly differentially expressed in the two tumour entities, of which 336 and 213 were overexpressed in ESS and LMS, respectively. Genes found to be overexpressed in ESS included CCND2, ECEL1, ITM2A, NPW, SLC7A10, EFNB3, PLAG1 and GCGR, whereas genes overexpressed in LMS included FABP3, TAGLN, CDKN2A, JPH2, GEM, NAV2 and RAB23. qPCR analysis confirmed these differences for 14 of 16 genes selected for validation. Five protein products were selected for validation by IHC, including the LMS markers fatty acid binding protein (FABP3), transgelin (TAGLN) and neuron navigator 2 (NAV2) and the ESS markers cyclin D2 (CCND2) and integral membrane protein 2A (ITM2A). All were found to be significantly differentially expressed in LMS vs ESS (Fig. 1) [11]. Data for FABP3, TAGLN, NAV2 and CCND2 were recently confirmed in a large (approx. 350 tumours) uterine sarcoma series [Davidson et al., manuscript submitted].
Recently, we compared the microRNA (miRNA) profiles of primary ESS (n=9), primary LMS (n=8) and metastatic LMS (n=8) using Taqman Human miRNA Array Cards. Ninety-four miRNAs were significantly differentially expressed in ESS vs LMS, of which 76 and 18 were overexpressed in ESS and LMS, respectively. Forty-nine miRNAs were differentially expressed in primary and metastatic LMS, among which 45 and 4 were overexpressed in primary and metastatic LMS, respectively. Twenty miRNAs found to be most significantly differentially expressed in primary ESS vs LMS or in primary vs metastatic LMS were further studied in a validation series of 44 tumours using qPCR. Of these, 10 were confirmed to be differentially expressed in these groups, including overexpression of 7 miRNAs (mir-15b, mir-21, mir-23b, mir-25, mir-145, mir-148b and mir-195) in ESS compared to primary LMS. The remaining 3 differentially expressed miRNAs were in comparative analysis of primary and metastatic LMS (lower mir-15a and mir-92a levels and higher mir-31 levels in primary LMS). Differentially expressed miRNA regulated the mitogen-activated protein kinase (MAPK) signaling pathway, Wnt signaling, focal adhesion, the mTOR signaling pathway and the transforming growth factor-β (TGF-β) signaling pathway. As Wnt signaling pathway genes are controlled by miRNAs 15a, 31 and 92a in LMS, we looked at the biological role of Frizzled-6 in LMS cells and found that Frizzled-6 silencing by siRNA significantly inhibited cellular invasion, wound closure and matrix metalloproteinase (MMP-2) activity [12]
Conclusion and future perspectives
Recent years have brought about considerable progress in our understanding of the molecular events occurring in ESS and LMS. Our studies and data from other groups may aid in the diagnosis and classification of these cancers, hopefully providing rationale for targeted therapy. Uterine sarcomas express different cancer-related molecules that may be targeted (reviewed by Cuppens et al. [13]). Anti-hormonal treatment is used in patients with hormone receptor-positive tumours, and expression of progesterone receptor was recently shown to be a prognostic marker in stage I LMS [14]. In two studies, targeting of mTOR, Aurora kinases and other mitotic checkpoint regulators has been suggested as therapeutic modality in LMS [15,16]. Additional studies are likely to identify new relevant targets in the future, hopefully improving the outcome of uterine sarcoma patients.
Acknowledgement
The work of Dr Davidson is supported by the National Sarcoma Foundation at the Norwegian Radium Hospital.
References
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3. Kurman RJ, Carcangiu ML, Herrington CS, Young RH (Eds.). WHO classification of tumours of female reproductive organs. IARC 2014.
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16. Shan W, Akinfenwa PY, Savannah KB, Kolomeyevskaya N, Laucirica R, Thomas DG, Odunsi K, Creighton CJ, Lev DC, Anderson ML. A small-molecule inhibitor targeting the mitotic spindle checkpoint impairs the growth of uterine leiomyosarcoma. Clin Cancer Res. 2012; 18: 3352–3365.
The author
Ben Davidson1,2 MD, PhD
1Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway
2University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, N-0316 Oslo, Norway
*Corresponding author
E-mail: bend@medisin.uio.no
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