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Featured Articles
Monitoring kidney function in aminoglycoside therapy
Aminoglycosides are antibiotics largely used at the hospital. Their nephrotoxicity imposes therapeutic drug monitoring as well as kidney function monitoring. Creatinine is the most widely used biochemical marker; however, new biomarkers such as neutrophil gelatinase associated lipocalin (NGAL), cystatin C (Cys C) or kidney injury molecule-1 (KIM-1) can allow the detection of acute kidney injury more quickly.
by F. Fraissinet, E. Sacchetto and Dr E. Bigot-Corbel
Background
Aminoglycosides are bactericidal antibiotics used for the treatment of Gram-negative or endocarditis infections. The most important adverse effects of aminoglycosides are nephrotoxicity and ototoxicity. The prevalence of aminoglycoside-associated nephrotoxicity is estimated at 10 to 20 %, although it also depends on the patient’s clinical condition and exposure to nephrotoxic drugs such as cyclosporine, anti-inflammatory or iodinated drugs. In intensive care units, nephrotoxicity is most frequent (60% of patients) and associated with a high rate of mortality [1, 2]. Nephrotoxicity mainly results in nonoliguric acute kidney injury which occurs following 7 to 10 days. This toxicity may manifest as a decrease in the glomerular filtration rate with glycosuria, hypokalemia and hypocalcemia. Aminoglycosides are often administered by intravenous drip and are freely eliminated by glomerular filtration and reabsorbed by the proximal tubule. Of the injected dose, 5% is retained by epithelial cells of the proximal tubule. After endocytosis in tubular cells, these molecules accumulate in lysosomes and induce phospholipidosis, alteration of key cellular components and apoptosis. Aminoglycosides also have glomerular effects: gentamicin stimulates mesangial proliferation, produces mesangial contraction and induces neutralization of negative charges of the glomerulus [3]. Gentamicin also induces a reduction in renal blood flow with an increased renal vascular resistance. These factors contribute to decrease the glomerular filtration rate (GFR). Additional risk factors for nephrotoxicity induced by aminoglycoside have been identified as sepsis, prolonged therapy, renal or liver dysfunction, hypokalemia or hypomagnesemia. Nephrotoxicity is less frequent when aminoglycosides are administered once daily compared with 12 h [4].
Methods of detection of acute kidney injury induced by aminoglycoside therapy
Classic markers: creatinine and creatinine clearance
Creatinine is the most widely used marker in the diagnosis of the acute renal insufficiency. For defining AKI, the Risk Injury Failure Loss End stage kidney disease (RIFLE) classification is based on increase of serum creatinine concentration and decrease of glomerular filtration rate. The introduction of the RIFLE classification has increased the conceptual understanding of AKI syndrome, and this classification has been successfully tested in a number of clinical studies [5].
In spite of an easily accessible dosage, creatinine as a marker of AKI has some drawbacks. Creatinine is filtered by the glomerulus and is not bound to plasma proteins. In standard physiological conditions, the daily rate of creatinine production is constant; however, the rate of creatinine production is affected by conditions of muscular pathology or muscular loss (as occurs in intensive care and cirrhosis). In these patients, AKI does not result in an increase of serum creatinine levels. Other factors, such as age, sex, ethnic group and diet, also influence serum concentrations of creatinine. Creatinine is not the ideal marker to estimate GFR, because it is secreted by renal tubule, which artefacutally increases glomerular filtration rate. At low serum creating concentrations, creatinine is lacks sensitivity to estimate GFR. Large changes in GFR may be associated with relatively small changes in serum creatinine (See Figure 2 in Delanaye et al. [6]). The rise of the creatinine is late (occurring after 3–5 days) and is not specific for nephrotoxicity induced by aminoglycosides, and an increase of creatinine in AKI is a function of the initial concentration of creatinine [7].
To estimate GFR, formulas that use creatinine plasma concentration, such as the Modified Diet in Renal Disease formula (MDRD), Chronic Kidney Disease Epidemiology collaboration formula (CKD-EPI) or estimation of clearance creatinine by Cockroft–Gault (CG) equation, were derived in subjects with chronic, not acute, kidney disease. A limitation of the MDRD equation was an underestimation of GFR in the high range. The CKD-EPI equation performs better at high GFR levels (GFR >60 mL/min/1.73 m²). Use of serum creatinine concentration to estimate GFR supposes a steady-state between creatinine production and excretion [8]. In spite of the use of correction factors, it is more difficult to estimate GFR in Asian or African populations as well as in elderly or obese patients [9].
New biomarkers
Cystatin C
Cystatin C (CysC), a 13-kDa endogenous cysteine proteinase inhibitor, plays an important role in intracellular catabolism of various peptides and proteins. CysC is considered to be a good biomarker of decreased kidney function because it is produced at a relatively constant rate and released into plasma, and is filtered by glomeruli without tubular secretion. The influence of muscular mass is less than for creatinine, and CysC allows diagnosis of AKI 48 h before serum creatinine [10]. Equations with serum CysC concentration can also estimate GFR. If GFR is great than 60 mL/min/m², CysC measurement is more powerful than the MDRD equation. CysC is a useful biomarker for early detection of AKI in the pediatric population and for patients in the intensive care unit, as CysC determination can be performed in serum and/or in urine. In spite of efforts to standardize the procedure, there is no reference method. Production of CysC also depends on hormonal factors, so CysC cannot be used in cases of thyroid dysfunction.
Neutrophil gelatinase associated lipocalin
Neutrophil gelatinase associated lipocalin (NGAL) is a protein of 25 kDa protein of the lipocalin family and is covalently bound to matrix metalloproteinase-9. NGAL is expressed early in ischemic kidney impairment in animal models. During AKI, NGAL expression is induced in distal nephron epithelia resulting in elevated plasma and urinary levels of NGAL (Fig. 1) [2]. NGAL determination can be performed on serum and/or urine by immunoturbimetric or immunofluorimetric assays. There is a general agreement on a cut-off value of >150 ng/mL, but a clear cut-off NGAL concentration for AKI has not been reported. Several studies show the importance of NGAL in cardiac surgery or critically ill patients for predicting AKI. NGAL is also useful for the detection of nephrotoxicity induced by contrast agents and has prognostic value for mortality or initiation of renal replacement therapy. Plasma NGAL measurements may be influenced by a number of coexisting variables as chronic hypertension, systemic infections, inflammatory conditions or hypoxia. Changes in NGAL values are potentially associated with septic state or aminoglycoside therapy [11].
Kidney injury molecule-1
Kidney injury molecule-1 (KIM-1) is a glycoprotein localized in the apical membrane of the proximal tubule of kidney, and KIM-1 expression can be induced by nephrotoxic drugs. Urine KIM-1 is a promising biomarker of proximal tubular injury. As with NGAL, urinary KIM-1 levels predicted adverse clinical outcomes such as dialysis requirement and mortality. In a previous study, urinary KIM-1 is correlated with AKI severity in non-critically ill children treated by aminoglycosides [12].
Conclusion
Patients treated with aminoglycosides must be carefully monitored for nephrotoxicity. Creatinine has been the most used biochemical marker of AKI, but new biomarkers, such as NGAL and KIM-1, have been developed in recent years.
References
1. Oliveira JF, Silva CA, Barbieri CD, Oliveira GM, Zanetta DM, Burdmann EA. Antimicrob Agents Chemother. 2009; 53(7): 2887-2891.
2. Schmidt-Ott KM. Nephrol Dial Transplant. 2011; 26(3): 762-764.
3. Lopez-Novoa JM, Quiros Y, Vicente L, Morales AI, Lopez-Hernandez FJ. Kidney Int. 2011; 79(1): 33-45.
4. Rybak MJ, Abate BJ, Kang SL, Ruffing MJ, Lerner SA, Drusano GL. Antimicrob Agents Chemother. 1999; 43(7): 1549-1555.
5. Ricci Z, Cruz DN, Ronco C. Nat Rev Nephrol. 2011; 7(4) :201-208.
6. Delanaye P, Cavalier E, Maillard N, Krzesinski JM, Mariat C, Cristol JP, et al. [Creatinine: past and present]. Annales de Biologie Clinique 2010; 68(5): 531-543 (in French).
7. Waikar SS, Bonventre JV. J Am Soc Nephrol. 2009; 20(3): 672-679.
8. Nguyen MT, Maynard SE, Kimmel PL. Clin J Am Soc Nephrol. 2009; 4(3): 528-34.
9. Delanaye P, Cavalier E, Mariat C, Krzesinski JM, Rule AD. Kidney Int. 2011; 80(5): 439-440.
10. Herget-Rosenthal S, Marggraf G, Husing J, Goring F, Pietruck F, Janssen O, et al. Kidney Int. 2004; 66(3): 1115-1122.
11. Devarajan P. Nephrology (Carlton) 2010; 15(4): 419-428.
12. McWilliam SJ, Antoine DJ, Sabbisetti V, Turner MA, Farragher T, Bonventre JV, et al. PLoS One 2012; 7(8): e43809.
The authors
François Fraissinet1 BSc, Emilie Sacchetto2 and Edith Bigot-Corbel2* PhD
1Laboratoire de Biochimie, 86021 Poitiers, France
2Laboratoire de Biochimie, CHU de Nantes, Hôpital G et R Laënnec, 44800 Saint-Herblain, France
*Corresponding author
E-mail: edith.bigot@chu-nantes.fr
A new approach for diagnosis of Clostridium difficile based on evolution of a unique volatile organic compound identifier
Clostridium difficile is a major cause of nosocomial infections and rapid diagnosis of the disease is essential for infection control. Several methods for C. difficile detection are employed in clinical laboratories; each method has its advantages and disadvantages. A novel method has recently been developed that allows differentiation between C. difficile-positive and -negative stool samples based on volatile organic compound evolution and their detection by headspace solid-phase microextraction gas chromatography–mass spectrometry.
by Dr Emma Tait, Prof. Stephen P. Stanforth, Prof John D. Perry and Prof. John R. Dean
Introduction
Clostridium difficile is a Gram-positive anaerobe and the causative agent of C. difficile infection (CDI). CDI is a major healthcare problem with a total of 14,687 cases reported in patients aged 2 years and over in England between April 2012 and March 2013 [1]. C. difficile is a spore-forming bacterium; dormant spores are resistant to antibiotics, heating and chemicals such as disinfectants and, therefore, can persist on surfaces and survive for long periods in the environment [2]. Ingestion of spores and their subsequent germination in the gut allows the proliferation of C. difficile in patients whose normal gut flora has been severely reduced following antibiotic treatment. Following germination, vegetative C. difficile can produce toxins and is susceptible to antibiotic treatment. Pathogenic C. difficile releases two types of toxins, toxin A and toxin B, and it is these toxins that cause the symptoms associated with CDI [3]. Clinical symptoms of C. difficile infection (CDI) include mild to severe diarrhoea, which can lead to pseudomembranous colitis and death. Diagnosis of CDI includes both clinical manifestations of symptoms supported by laboratory findings.
Diagnosis of C. difficile infection
Rapid diagnosis of CDI is essential to allow the most appropriate treatment to be prescribed, to enable proper use of hospital isolation facilities and to reduce the spread of the infection. Routine diagnostic methods in clinical microbiology laboratories employ a variety of techniques for diagnosis of CDI. These include immunoassays for detection of glutamate dehydrogenase (GDH) antigen and toxins, polymerase chain reaction (PCR) for detection of toxin B or isolation of C. difficile by culture (followed by confirmation of toxigenicity, e.g. using PCR). Immunoassays and PCR methods are typically highly automated and deliver rapid results and these have largely replaced the traditional cell cytotoxicity assay, which requires propagation of cell lines and takes days rather than hours to provide results.
Toxin immunoassays, although relatively inexpensive with rapid turnaround time, have limitations in terms of their sensitivity and specificity leading to false-negative results and false-positive results [4]. Immunoassays for GDH are typically highly sensitive for detection of C. difficile but lack specificity. Culture media for the isolation of C. difficile typically incorporate the antibiotics D-cycloserine and cefoxitin which suppress commensal bacteria; such media are based on the formulation recommended by George et al. [5]. Isolation of C. difficile via culture is sensitive but can take several days to obtain results and there may be a heavy growth of other fecal bacteria, particularly when reduced antibiotic concentrations are used [6]. Recent developments in C. difficile detection include using a chromogenic substrate which is structurally similar to naturally occurring substrate used by C. difficile toxins [7]. This allows the detection of toxigenic C. difficile, i.e. only pathogenic strains are targeted.
Identification of C. difficile-positive stool samples using gas chromatography has previously been explored [8]; volatile organic compounds (VOCs) such as p-cresol and short chain fatty acids were identified as potential markers for C. difficile. However, these methods suffered from a lack of specificity, particularly due to the high number of false positives obtained following the detection of p-cresol and isocaproic acid in stool samples without C. difficile. As a consequence, gas chromatography methods were deemed unsuitable for C. difficile detection [8]. More recent attempts to use bacterial VOC analysis as a tool for C. difficile identification have used headspace solid-phase microextraction (HS-SPME) as a VOC collection method coupled with gas chromatography–mass spectrometry (GC-MS) for VOC separation and detection [9].
Development of a novel method for detection of C. difficile in stools
A novel method for rapid identification of C. difficile in stool samples has been developed using the analysis of VOCs. Use of synthetic enzyme substrates is an effective means of differentiating bacteria, for example in chromogenic culture media [10]. These types of culture media incorporate chromogenic enzyme substrates where the action of a specific enzyme on the substrate liberates a molecule that is detectable visually, allowing the detection of pathogenic bacteria [10]. The philosophy behind the use of substrates in culture media can be applied to the analysis of bacterial VOCs, where a substrate is incorporated into a clinical sample inoculated in liquid media; the cleaved product is volatile and detectable using an analytical method such as HS-SPME-GC-MS. The detection of VOCs liberated following enzyme activity increases the specificity of bacterial VOC profiles, as these liberated VOCs act as markers for a particular species, hence aiding identification of bacteria. This approach was applied to the detection of C. difficile in stool samples.
p-Cresol is formed in C. difficile by the decarboxylation of p-hydroxyphenylacetic acid. The enzyme responsible for this decarboxylation is p-hydroxyphenylacetate decarboxylase. It has been established that the hydroxyl group in the para position on the phenyl ring is an essential requirement for decarboxylation to occur [11]. C. difficile is almost unique in its ability to form p-cresol using this pathway, with the exception of a Lactobacillus strain [12]. This was exploited in the development of the novel method that allows successful differentiation between C. difficile culture-positive and -negative stool samples based on VOC generation from an enzyme substrate [13]. 3-Fluoro-4-hydroxyphenylacetic acid was used as a substrate for p-hydroxyphenylacetate decarboxylase; the evolution of the VOC 2-fluoro-4-methylphenol indicated the presence of C. difficile (Fig. 1). VOCs were detected using HS-SPME-GC-MS.
The lack of specificity of previous GC methods was often due to the detection of VOCs in C. difficile culture-negative stool samples as these VOCs were generated by commensal bacteria. Techniques employed to reduce background flora, and therefore improve the selectivity and sensitivity of methods, include alcohol shock [14] and the inclusion of antibiotics [5]. The antibiotics D-cycloserine, cefoxitin and amphotericin were added to the sample matrix and an alcohol-shock step was included to suppress background flora present in stool samples. Alcohol shock kills vegetative cells but does not affect the viability of spores and incorporation of sodium taurocholate in a culture medium can subsequently aid germination of spores [6, 15]. Inoculation of stool samples into such a culture medium allows bacterial growth and concomitant generation of VOCs.
The method was tested with 100 stool samples, of which 77 were C. difficile culture-positive and 23 culture-negative. The generation of 2-fluoro-4-methylphenol indicated the presence of C. difficile after overnight incubation. Method specificity and sensitivity were 100 % and 83.1 %, respectively, using 2-fluoro-4-methylphenol as a marker for C. difficile identification (Table 1). The VOCs isocaproic acid and p-cresol were useful indicators for C. difficile-positive stool samples, although were insufficient for identification purposes. Both VOCs, particularly p-cresol, were generated by C. difficile-negative samples; this is in agreement with previous studies [8].
Advantages and disadvantages of VOC method
The method allows the detection of C. difficile with a very high specificity (100%), i.e. 2-fluoro-4-methylphenol was not generated by C. difficile culture-negative stool samples tested. Rapid detection of VOCs was possible with confirmation of the presence of C. difficile within 18 hours. This indicates that the method could be used to screen for C. difficile in stools allowing the prompt diagnosis of culture-positive samples by the detection of 2-fluoro-4-methylphenol. However, a study on method sensitivity in terms of the number of bacterial cells required to generate a positive signal confirmed that identification of C. difficile was possible provided the stool sample contained at least 150 colony forming units (CFU). It is entirely possible that some stool samples will contain much fewer CFU and therefore 2-fluoro-4-methylphenol would not be detected and a false-negative result would be obtained. This limitation is reflected in the method sensitivity (83.1%) after evaluation with 100 stool samples. The method targets all strains of C. difficile and further testing would be required (e.g. using PCR or immunoassay) to distinguish whether positive stool samples contain toxigenic strains. As a result, it is recommended that VOC analysis should be used alongside conventional methods for C. difficile detection, including toxin detection methods, which would allow any false negative results to be eliminated.
Conclusion
C. difficile is a common cause of nosocomial infections and therefore rapid, accurate diagnosis of CDI is of extreme importance for infection control and patient care. There are currently a number of methods used in hospital laboratories for the diagnosis of CDI; however, each method has its drawbacks. A novel approach has been developed for the identification of C. difficile in stool samples that involves the incubation of stool samples in the presence of 3-fluoro-4-hydroxyphenylacetic acid which acts as a substrate for the enzyme p-hydroxyphenylacetate decarboxylase. The success of this new approach is evaluated by its application to 100 stool samples and its ability to differentiate between C. difficile culture-positive and -negative stool samples. It is envisaged that the identification of C. difficile culture-positive stool samples by the analysis of VOCs could allow rapid diagnosis of CDI. In addition, the novel approach of using enzyme substrates that release VOCs that are not normally generated by bacteria, for example fluorinated VOCs, may find application in the identification of other bacterial pathogens in clinical microbiology.
References
1. Public Health England. Summary Points on Clostridium difficile Infection (CDI). 2013; http://www.hpa.org.uk/webc/HPAwebFile/HPAweb_C/1278944283388.
2. Kuijper EJ, Coignard B, Tull P. Emergence of Clostridium difficile-associated disease in North America and Europe. Clin Microbiol Infect. 2006; 12(Suppl 6): S2−S18.
3. Voth DE, Ballard JD. Clostridium difficile toxins: mechanism of action and role in disease. Clin Microbiol Rev. 2005; 18: 247–263.
4. Eastwood K, Else P, Charlett A, Wilcox M. Comparison of nine commercially available Clostridium difficile toxin detection assays, a real-time PCR assay for C. difficile tcdB, and a glutamate dehydrogenase detection assay to cytotoxin testing and cytotoxigenic culture methods. J Clin Microbiol. 2009; 47: 3211–3217.
5. George WL, Sutter VL, Citron D, Finegold SM. Selective and differential medium for isolation of Clostridium difficile. J Clin Microbiol. 1979; 9: 214–219.
6. Nerandzic MM, Donskey CJ. Effective and reduced-cost modified selective medium for isolation of Clostridium difficile. J Clin Microbiol. 2009; 47: 397–400.
7. Darkoh C, Kaplan HB, DuPont HL. Harnessing the glucosyltransferase activities of Clostridium difficile for functional studies of toxins A and B. J Clin Microbiol. 2011; 49: 2933–2941.
8. Levett PN. Detection of Clostridium difficile in faeces by direct gas liquid chromatography. J Clin Pathol. 1987; 37: 117–119.
9. Garner CE, Smith S, Costello BL, White P, Spencer R, Probert CSJ, Ratcliffe NM. Volatile organic compounds from feces and their potential for diagnosis of gastrointestinal disease. Faseb J. 2007; 21: 1675–1688.
10. Orenga S, James AL, Manafi M, Perry JD, Pincus DH. Enzymatic substrates in microbiology. J Microbiol Meth. 2009; 79: 139–155.
11. Selmer T, Andrei PI. p-Hydroxyphenylacetate decarboxylase from Clostridium difficile. A novel glycyl radical enzyme catalysing the formation of p-cresol. Eur J Biochem. 2001; 268: 1363–1372.
12. Yokoyama MT, Carlson JR. Production of skatole and para-cresol by a rumen Lactobacillus sp. Appl Environ Microbiol. 1981; 41; 71–76.
13. Tait E, Hill KA, Perry JD, Stanforth SP, Dean JR. Development of a novel method for detection of Clostridium difficile using HS-SPME-GC-MS. J Appl Microbiol. DOI: 10.1111/jam.12418.
14. Clabots CR, Gerding SJ, Olson MM, Peterson LR, Gerding DN. Detection of asymptomatic Clostridium difficile carriage by an alcohol shock procedure. J Clin Microbiol. 1989; 27: 3286–3287.
15. Wilson KH, Kennedy MJ, Fekety FR. Use of sodium taurocholate to enhance spore recovery on a medium selective for Clostridium difficile. J Clin Microbiol. 1982; 15; 443–446.
The authors
Emma Tait1 PhD, Stephen P. Stanforth1 PhD, John D. Perry2 PhD and John R. Dean1* DSc, PhD
1Faculty of Health & Life Sciences, Department of Applied Sciences, Northumbria University, Newcastle-upon-Tyne, UK
2Department of Microbiology, Freeman Hospital, Newcastle-upon-Tyne, UK
*Corresponding author
E-mail: John.dean@northumbria.ac.uk
Uncovering molecular biomarkers in gastric adenocarcinoma
Gastric adenocarcinoma is usually diagnosed at an advanced stage, which portends a poor prognosis. Molecular biomarkers are important tools to understand the underlying biology of its aggressive behaviour and to discover new targets for therapeutic agents. Microarray analyses and next generation sequencing are leading to a deeper understanding of tumour biology and the development of new biomarkers, offering hope for better treatment approaches in the future.
by Dr I. Snitcovsky, Dr F. Solange Pasini and Dr G. de Castro Jr
Background
Gastric cancer is the fourth most common cancer in the world and is especially prevalent in East Asia and South America [1]. Adenocarcinoma accounts for the great majority of these tumours, which are classified as intestinal or diffuse type. The pathogenesis is incompletely understood, but it is associated with Helicobacter pylori infection and dietary salt and nitrosamines, particularly in intestinal type tumours. In these cases, chronic inflammation is thought to lead to preneoplastic lesions that may progress to invasive cancer in a stepwise fashion. In a minority of cases, germline mutations of P53, CDH1 and mismatch repair genes are associated with familial cases. The most important prognostic factor is the tumour TNM stage, since the only curative approach is surgical resection, followed (or not) by adjuvant therapies. Thus, locally advanced and metastatic disease portends a poor prognosis, with a median survival of less than one year. Unfortunately, most patients in Western countries are diagnosed with advanced disease, and, in these cases, chemotherapy can palliate symptoms and prolong overall survival but it is not curative [2]. In patients with metastatic disease, the only biomarker routinely tested for in gastric adenocarcinoma is HER2 (human epidermal growth factor 2 receptor; by immunohistochemistry), which is associated with poor outcomes and is also predictive of the anti-tumour efficacy of the humanized anti-HER2 antibody, trastuzumab [3].
The development of innovative treatment approaches begins with the identification of molecular biomarkers relevant to tumour biology. The next step is clinical validation, usually by showing that the studied biomarker has a prognostic value. Finally, a targeted agent is developed and shown to prolong survival in phase III clinical trials. Genome-wide studies are revealing potential biomarkers for targeted therapies and immunotherapy. This review will focus on recently identified candidate biomarkers in gastric adenocarcinoma with potential clinical applications.
Biomarkers in the pre-genomic era
Cancer cells are characterized by self-sufficiency in growth signals, insensitivity to anti-growth signals, evasion of apoptosis, limitless reproductive potential, sustained angiogenesis and tissue invasion and metastasis [4]. Accordingly, in gastric adenocarcinoma, a great number of studies focused on the prognostic role of single molecules. They included, but were not restricted to, growth factors and their receptors [HER2, IGFR (insulin-like growth factor 1 receptor)], cell cycle regulators (p53), angiogenesis controllers [VEGF (vascular endothelial growth factor)] and matrix metalloproteinases, with so far no impact in patient management, with the exception of HER2.
The epidermal growth factor receptor (EGFR) family includes HER1 (EGFR), HER2 (ErbB2), HER3 (ErbB3) and HER4 (ErbB4). These molecules form dimers on the cell surface after ligand binding, which leads to intracellular signalling that modulates cell proliferation, metastasis and angiogenesis. HER2 has no known ligands, but forms heterodimers with other members of the HER family and potentiates signalling. In breast cancer, HER2 overexpression is related to poor prognosis and the humanized anti-HER2 monoclonal antibody trastuzumab prolongs survival in those patients with HER2 positive tumours [5]. In gastric adenocarcinoma, HER2 overexpression is detected in 9–35% of cases and implies a worse prognosis in some studies. A phase III trial that included patients with HER2-overexpressing gastric adenocarcinoma found that the addition of trastuzumab to chemotherapy resulted in an overall survival benefit of about two months, as compared to chemotherapy alone [3]. In contrast, phase III studies evaluating anti-angiogenic agents in unselected gastric adenocarcinoma patients presented conflicting results [6, 7].
Gene panels and next generation sequencing
Gastric adenocarcinoma is a heterogeneous disease, thus the simultaneous determination of several biomarkers may be more informative then single ones, nowadays possible by high-throughput technologies as microarray platforms and next generation sequencing. Chen et al. [8] proposed a prognostic three-gene model, derived from gene expression profiling in eighteen paired samples. Marchet et al. [9] proposed another three-gene model predictive of lymph node involvement in a cohort of 32 patients. Another prognostic four-gene signature was also described [10]. Little overlap was observed among these above-mentioned signatures, which is not informative in terms of advancing in the cancer biomarker development.
A study comparing 248 gastric adenocarcinoma tumour samples was able to classify tumours in three subtypes, based on gene expression patterns: proliferative, metabolic and mesenchymal. In addition, these subtypes were shown to have differences in molecular and genetic features, and response to therapy [11]. Next generation sequencing is providing a deeper level of understanding the tumour biology. Genetic alterations were observed in Wnt, Hedgehog, cell cycle, DNA damage and epithelial-to-mesenchymal-transition pathways by analysing the genome and the transcriptome in 50 adenocarcinoma samples. About 20% of these alterations could be considered as potential targets for drugs that are already available [12]. Novel fusion genes were identified, especially DUS4L-BCAP29, when transcriptome sequencing was performed in 12 gastric adenocarcinoma cell lines. Knockdown of this transcript inhibited cell proliferation, thus validating its functional role [13].
Immune biomarkers
Cancer, including gastric adenocarcinoma, is viewed as a tissue disease. This implies that the microenvironment plays a key role in tumour biology [4]. Thus, immune cell infiltrate has been shown to be of prognostic value in gastric adenocarcinoma. As depicted in Figure 1, tumour-associated macrophages present two different polarizations: classical (M1) characterized by immunostimulation activity and tumour suppression; and alternative (M2) characterized by tumour promotion and immune suppression. A higher ratio of M1/M2 macrophages was associated with a favourable prognosis [14]. The underlying mechanism is complex, but may involve growth factor modulation [15]. We conducted a gene expression study, including a total of 51 freshly frozen tumour samples from patients with gastric adenocarcinoma treated with surgery. An immune-related gene trio (OLR1, CXCL11 and ADAMDEC1) was identified as an independent biomarker of prognosis. We proposed that immune dampening in the tumour microenvironment was present in patients with poor prognosis. Three main observations supported our hypothesis. First, the expression levels of genes belonging to the functional group of immune/inflammatory response were markedly reduced as a whole. Second, a network analysis suggested an unwired inflammatory response, and third, a decreased expression of type-1 helper lymphocyte (Th1) and other immune activating genes was found [16]. The biomarkers we identified may be good candidates for selecting patients for immunomodulation therapies, including immune checkpoint inhibitors [17].
Conclusions and perspectives
Gastric adenocarcinoma needs better treatment approaches. New technologies are offering the necessary tools to identify molecular biomarkers, leading to a deeper understanding of tumour biology and the development of innovative treatment strategies, and we are entering an era of cautious optimism. Considering the tumour heterogeneity and the limited survival gains with targeted agents in solid tumours, it is possible that patient selection by immune biomarkers and the use of immune checkpoint inhibitors are promising alternatives. The impressive response rates and overall survival benefits observed in patients with squamous cell lung cancer and melanoma, two notoriously chemoresistant tumours, when treated with anti-PD1 or anti-PD1L are good examples [18].
References
1. Ferlay J, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11; 2013. (http://globocan.iarc.fr)
2. Waddell T, et al. Gastric cancer: ESMO-ESSO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2013;24(Suppl 6):57.
3. Bang YJ, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer. Lancet 2010;376: 687.
4. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646.
5. Figueroa-Magalhães MC, et al. Treatment of HER2- positive breast cancer. Breast 2013;doi:10.1016/j.breast.2013.11.011.
6. Ohtsu A, et al. Bevacizumab in combination with chemotherapy as first-line therapy in advanced gastric cancer: a randomized, double-blind, placebo-controlled phase III study. J Clin Oncol. 2011;29:3968.
7. Fuchs CS, et al. Ramucirumab monotherapy for previously treated advanced gastric or gastro-oesophageal junction adenocarcinoma (REGARD): an international, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet 2014;383:31.
8. Chen CN, et al. Gene expression profile predicts patient survival of gastric cancer after surgical resection. J Clin Oncol. 2005;23:7286.
9. Marchet A, et al. Gene expression profile of primary gastric cancer: towards the prediction of lymph node status. Ann Surg Oncol. 2007;14:1058.
10. Xu ZY, et al. Gene expression profile towards the prediction of patient survival of gastric cancer. Biomed Pharmacother. 2010;64:133.
11. Lei Z, et al. Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology 2013; 145:554.
12. Holbrook JD, et al. Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med. 2011;9:119.
13. Kim HP, et al. Novel fusion transcripts in human gastric cancer revealed by transcriptome analysis. Oncogene 2013;doi:10.1038/onc.2013.490.
14. Pantano F, et al. The role of macrophages polarization in predicting prognosis of radically resected gastric cancer patients. J Cell Mol Med. 2013;17:1415.
15. Cardoso AP, et al. Macrophages stimulate gastric and colorectal cancer invasion through EGFR Y(1086), c-Src, Erk1/2 and Akt phosphorylation and smallGTPase activity. Oncogene 2013;doi:10.1038/onc.2013.154.
16. Pasini FS, et al. A gene expression profile related to immune dampening in the tumor microenvironment is associated with poor prognosis in gastric adenocarcinoma. J Gastroenterol. 2013;doi:10.1007/s00535-013-0904-0.
17. Eggermont AM, et al. Immunotherapy and the concept of a clinical cure. Eur J Cancer 2013;49: 2965.
18. Brahmer JR, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012;366:2455.
The authors
Igor Snitcovsky1,2 MD, PhD; Fátima Solange Pasini1,2 PhD; and Gilberto de Castro Jr*1,3 MD, PhD
1 Departamento de Radiologia e Oncologia, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
2 Centro de Investigação Translacional em Oncologia, Instituto de Câncer do Estado de São Paulo (ICESP), São Paulo, Brazil
3 Oncologia Clínica, Instituto do Câncer do Estado de São Paulo (ICESP), São Paulo, Brazil
*Corresponding author
E-mail: gilberto.castro@usp.br
