As the appearance of circulating tumour cells in the peripheral blood of breast cancer patients is linked to a worse prognosis for overall survival and treatment efficiency, their detection and characterization will have a high impact on cancer therapy, opening roads to a more personalized treatment.
by Dr U. Andergassen, Dr A. C. Kölbl, Prof. K. Friese and Prof. U. Jeschke
Circulating tumour cells
Already in 1869 the occurrence of cancer cells in the peripheral blood of a metastatic cancer patient was described by Thomas Ashworth. Nowadays it is well known, that cells dissolve from primary epithelial tumours such as breast, lung, colon or prostate cancer, enter circulation and travel via the blood stream or lymphatic system throughout the whole body. If these cells [termed circulating tumour cells (CTCs)] leave circulation, they can settle at other sites in the body and are then considered to be the main reason for the generation of remote metastasis. Their appearance is linked to a poorer outcome of cancer therapy and to a worse prognosis for overall survival. Therefore, the detection of CTCs in peripheral blood [and of disseminated tumour cells (DTCs) in bone marrow] was already included into the international tumour staging systems.
Unfortunately the detection of CTCs is still a technical challenge, as the number of tumour cells in the blood stream is rather small (1 in 106–7 blood cells). To date, there is only one FDA-approved system for CTC detection, at least in the metastatic situation. This is the Cell Search® system (Veridex LLC.), which is based on immunomagnetic enrichment and simultaneous staining of tumour cells of epithelial surface markers, the cytokeratins. The huge disadvantage of this system is that it is rather expensive and, therefore, not yet routinely used in the clinic.
Real-time PCR in cancer cell detection
Another promising approach for CTC detection could be a real-time PCR-based method. The principle of this methodology is that breast-cancer CTCs are derived from an epithelial tumour, and, therefore, express a panel of epithelial cell genes. The surrounding blood cells in contrast are of mesenchymal origin, showing different gene expression profiles. Thus, it can be assumed that tumour cells are present in a given blood sample if the expression of epithelial genes is higher than in a negative control sample.
Real-time PCR measures gene expression levels by detecting an increase of fluorescence due to the incorporation of fluorescent reporter molecules into the newly synthesized DNA molecules during the PCR reaction. If a gene is highly expressed, a lot of mRNA of this gene is present, meaning plenty target for PCR reaction is available and thus influencing the fluorescence level measured at the end of each amplification cycle. The time point when fluorescence reaches a certain threshold is called the Ct-value, and this is the basis of the calculation of relative gene expression values by the 2-∆∆Ct-method [1]. In brief: the average Ct-value of a gene of interest is related to the average Ct-value of a reference gene. The resulting value is called the ΔCt-value. In the next step, this ΔCt-value is set in reference to the ΔCt-value of the same gene in the reference sample, rendering the so called ΔΔCt-value. The formula 2–ΔΔCt is then used to calculate relative quantification (RQ) values. RQ values >1 show an upregulation of the gene of interest, values <1 mean that the gene is downregulated.
Spiking experiments
The first step towards a real time PCR based quantitative cancer diagnosis is to create calibration curves for the used marker genes to evaluate the number of cancer cells exhibited at a certain level of gene expression in a blood sample. Therefore, blood samples of healthy donors, to which a certain number of cells from a breast-cancer cell line were added, were used to create standard curves. For this evaluation different breast-cancer cell lines were used (Cama-1, MCF-7, MDA-MB231 and ZR-75-1), and real-time PCR was carried out for Cytokeratin 8, 18 and 19 as marker genes [2, 3]. Cancer cells were added in rising numbers and calibration curves could be drawn [Fig. 1], showing an increase in gene expression level from 10 cells added to a blood sample upwards, meaning that even a small number of cancer cells in the blood (resembling the ‘real’ conditions, with 1 CTC per106–7 surrounding blood cells) can be detected by this methodology.
PCR marker genes for CTC detection
As CTCs in the blood are rare, PCR marker genes have to be selected as accurately as possible. The first choice are the Cytokeratin (CK) genes 8, 18 and 19, as they are also used in the routinely applicated APAAP-staining, which is a histochemical detection method for CTCs. The cytokeratin family members are characteristic epithelial cell markers and only weakly expressed in blood cells, rendering them potentially useful for PCR-based detection of CTCs.
Three other genes (BCSP, MGL, Her2) were selected and used in an approach to detect differences in gene expression between normal individuals and adjuvant and metastatic breast-cancer patients [4]. Mammaglobin (MGL) is a gene which is only expressed in the adult mammary gland and is known to be upregulated in breast cancer [5]. Breast cancer specific protein (BCSP) is highly expressed in advanced infiltrating breast cancer and is a marker for recurrence of the disease and formation of metastases [6], and c-erbB2 (Her2) was used, because it is over-expressed in 20% of breast cancers and is also responsible for the aggressiveness of the tumour [7].
These markers were comparatively analysed in blood samples withdrawn from adjuvant and metastatic breast-cancer patients during surgery. The gene expression levels of adjuvant as well as metastatic breast-cancer patients were normalized to levels in blood samples from 20 healthy donors, considered as a negative control group. Differences in gene expression between the three sample groups were detected [Fig. 2] and it was attempted to find a signature of marker genes for CTCs in breast cancer by real-time PCR.
From the experiments, it could be concluded that cytokeratin genes seem to be the most promising markers for the detection of CTCs from peripheral blood of breast-cancer patients with reverse-transcription real-time PCR. The most suitable marker of the cytokeratin array is apparently CK8, rendering most expression values >1.
MGL, BCSP, and Her2 mRNA show few expression values >1 as well in adjuvant as in metastatic patients. Altogether, higher amplitudes for these three genes were detected in the adjuvant setting. CTCs can be detected from peripheral blood by real-time-PCR, using the cytokeratin markers, especially cytokeratin 8.
In contrast to these findings are the results published by Obermayr et al. 2010 [8], who found an overexpression of MGL/hMAM in 39% of the examined advanced breast cancer cases. But they also conclude that using more marker genes for CTC detection results in a higher percentage of detected cancer cases. The same findings were obtained by [9], who also used a real-time PCR-based approach for CTC detection. They used CK19, SCGB2A2, MUC1, EPCAM, BIRC5 and Her-2 as marker genes and found a high sensitivity and specificity (56.3% and 100% respectively).
Additionally CK20 was identified as a promising marker gene [10] and seems to be correlated with the aggressiveness of the tumour. To further improve the detection of CTCs by real-time-PCR, more marker genes need to be tested; promising candidates are, for example, MMP13 [11], UBE2Q2 [12],
Nectin-4 [13], and ALDH [14].
Future directions for cancer therapy
Real-time PCR-based techniques were already used for solid tumour profiling and are considered to be objective, robust and cost-effective molecular techniques that could be used in routine cancer diagnosis. In future, a real-time PCR assay for the detection of circulating tumour cells from peripheral blood could find its way into modern medicine. This would be advantageous for the patient by limiting the number of invasive procedures, such as biopsies or bone marrow aspirations, that have to be undertaken to produce samples for analysis.
Furthermore by implication of more marker genes a characterization of tumour cells could be pursued, which already gives hints towards a cancer prognosis, as for example Bölke et al. described, that the expression of certain genes is correlated to advanced breast cancer stages [15]. A better knowledge of cancer properties in turn will help to apply a more personalized therapy, side effects can be reduced and treatment efficiency will strongly increase.
References
1. Livak KJ, Schmittgen TD. Methods 2001; 25(4): 402–408.
2. Zebisch M, Kolbl AC, Schindlbeck C, Neugebauer J, Heublein S, Ilmer M, Rack B, Friese K, Jeschke U, Andergassen U. Anticancer Res 2012; 32(12): 5387–5391.
3. Zebisch M, Kölbl AC, Andergassen U, Hutter S, Neugebauer J, Engelstädter V, Günthner-Biller M, Jeschke U, Friese K. Biomedical reports; accepted for publication 2012.
4. Andergassen U, Hofmann S, Kolbl AC, Schindlbeck C, Neugebauer J, Hutter S, Engelstadter V, Ilmer M, Friese K, Jeschke U. Int J Mol Sci 2013; 14(1): 1093–1104.
5. Fleming TP, Watson MA. Ann N Y Acad Sci 2000; 923: 78–89.
6. Wu K, Weng Z, Tao Q, Lin G, et al. Cancer Epidemiol Biomarkers Prev 2003; 12(9): 920–925.
7. Kim YS, Konoplev SN, Montemurro F, Hoy E, Smith TL, et al. Clin Cancer Res 2001; 7(12):4008–4012.
8. Obermayr E, Sanchez-Cabo F, Tea MK, Singer CF, Krainer M, et al. BMC Cancer 2010; 10: 666.
9. de Albuquerque A, Kaul S, Breier G, Krabisch P, Fersis N. Breast Care (Basel) 2012; 7(1): 7–12.
10. Tunca B, Egeli U, Cecener G, Tezcan G, Gokgoz S, Tasdelen I, et al. Tumori 2012; 98(2): 243–251.
11. Chang HJ, Yang MJ, Yang YH, Hou MF, Hsueh EJ, Lin SR. Oncol Rep 2009; 22(5): 1119–1127.
12. Nikseresht M, Seghatoleslam A, Monabati A, et al. Cancer Genet Cytogenet 2010; 197(2): 101–106.
13. Fabre-Lafay S, Garrido-Urbani S, Reymond N, et al. J Biol Chem 2005; 280(20): 19543–19550.
14. Dontu G. Breast Cancer Res 2008; 10(5): 110.
15. Bolke E, Orth K, Gerber PA, Lammering G, Mota R, et al. Eur J Med Res 2009; 14(8): 359–363.
The authors
Ulrich Andergassen* MD, Alexandra C. Kölbl PhD, Klaus Friese MD, and Udo Jeschke PhD
Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe, Ludwig Maximilian University of Munich, Munich, Germany
*Corresponding author
ulrich.andergassen@med.uni-muenchen.de
Legionella V-TesT
, /in Featured Articles /by 3wmediaLooking For Reliable And High Quality Respiratory Tests?
, /in Featured Articles /by 3wmediaBreakthrough In StatStrip Glucose Sensor Accuracy
, /in Featured Articles /by 3wmediaThe effects of tobacco smoke: first the bad news
, /in Featured Articles /by 3wmediaIt was over sixty years ago that Sir Richard Doll’s pioneering work first demonstrated a causal link between tobacco smoking and an increased risk of lung cancer. The lessons drawn from it have undoubtedly saved millions of lives over the years, but it is disappointing that according to the recently published European cancer mortality predictions for 2013, lung cancer remains the biggest cause of cancer death in male EU residents, and is predicted to become the biggest cause of cancer mortality in women in the near future, overtaking deaths from breast cancer.
The trend is similar in the US. A recently published paper in the New England Medical Journal, which involved data from more than two million women at three different time periods, showed that women who smoke currently are at a far greater risk of death from lung cancer than were women who smoked in the 1960s and the 1980s; the risk is now equal for both genders. While other factors that increase the risk of lung cancer, such as asbestos and radon gas exposure, have now been identified, tobacco smoke is still thought to be responsible for around 90% of lung cancer cases.
During the decades since Doll’s work it has, of course, been demonstrated that the risk of death from many other diseases, including other cancers, ischemic heart disease, stroke, chronic obstructive pulmonary disease and asthma, is augmented by smoking tobacco. More recently it has been recognized that passive smoking can also increase the risk of smoking-related diseases, and that prenatal exposure to tobacco smoke increases the risk of low birth weight and premature neonates, as well as SIDS and asthma in infancy. But in spite of the concerted efforts that have been made to educate the public about the dangers of tobacco smoke over more than half a century, a substantial minority of the population, including many physicians, still smokes.
Now for the good news. Several comparative studies indicate that public smoking bans now operating in much of the developed world are already affecting the rate of cardiovascular and respiratory disease. And a very recent robust study from Belgium, giving data from the three phases of the ban in that country, where smoking was first prohibited in the workplace (2006), then in restaurants (2007) and finally in bars serving food (2010), demonstrates a fall in the premature birth rate after each phase. So finally at least those of us who have heeded the oft-repeated health message may benefit fully from our prudence!
Detection of circulating tumour cells from peripheral blood of breast cancer patients via real-time PCR
, /in Featured Articles /by 3wmediaAs the appearance of circulating tumour cells in the peripheral blood of breast cancer patients is linked to a worse prognosis for overall survival and treatment efficiency, their detection and characterization will have a high impact on cancer therapy, opening roads to a more personalized treatment.
by Dr U. Andergassen, Dr A. C. Kölbl, Prof. K. Friese and Prof. U. Jeschke
Circulating tumour cells
Already in 1869 the occurrence of cancer cells in the peripheral blood of a metastatic cancer patient was described by Thomas Ashworth. Nowadays it is well known, that cells dissolve from primary epithelial tumours such as breast, lung, colon or prostate cancer, enter circulation and travel via the blood stream or lymphatic system throughout the whole body. If these cells [termed circulating tumour cells (CTCs)] leave circulation, they can settle at other sites in the body and are then considered to be the main reason for the generation of remote metastasis. Their appearance is linked to a poorer outcome of cancer therapy and to a worse prognosis for overall survival. Therefore, the detection of CTCs in peripheral blood [and of disseminated tumour cells (DTCs) in bone marrow] was already included into the international tumour staging systems.
Unfortunately the detection of CTCs is still a technical challenge, as the number of tumour cells in the blood stream is rather small (1 in 106–7 blood cells). To date, there is only one FDA-approved system for CTC detection, at least in the metastatic situation. This is the Cell Search® system (Veridex LLC.), which is based on immunomagnetic enrichment and simultaneous staining of tumour cells of epithelial surface markers, the cytokeratins. The huge disadvantage of this system is that it is rather expensive and, therefore, not yet routinely used in the clinic.
Real-time PCR in cancer cell detection
Another promising approach for CTC detection could be a real-time PCR-based method. The principle of this methodology is that breast-cancer CTCs are derived from an epithelial tumour, and, therefore, express a panel of epithelial cell genes. The surrounding blood cells in contrast are of mesenchymal origin, showing different gene expression profiles. Thus, it can be assumed that tumour cells are present in a given blood sample if the expression of epithelial genes is higher than in a negative control sample.
Real-time PCR measures gene expression levels by detecting an increase of fluorescence due to the incorporation of fluorescent reporter molecules into the newly synthesized DNA molecules during the PCR reaction. If a gene is highly expressed, a lot of mRNA of this gene is present, meaning plenty target for PCR reaction is available and thus influencing the fluorescence level measured at the end of each amplification cycle. The time point when fluorescence reaches a certain threshold is called the Ct-value, and this is the basis of the calculation of relative gene expression values by the 2-∆∆Ct-method [1]. In brief: the average Ct-value of a gene of interest is related to the average Ct-value of a reference gene. The resulting value is called the ΔCt-value. In the next step, this ΔCt-value is set in reference to the ΔCt-value of the same gene in the reference sample, rendering the so called ΔΔCt-value. The formula 2–ΔΔCt is then used to calculate relative quantification (RQ) values. RQ values >1 show an upregulation of the gene of interest, values <1 mean that the gene is downregulated. Spiking experiments
The first step towards a real time PCR based quantitative cancer diagnosis is to create calibration curves for the used marker genes to evaluate the number of cancer cells exhibited at a certain level of gene expression in a blood sample. Therefore, blood samples of healthy donors, to which a certain number of cells from a breast-cancer cell line were added, were used to create standard curves. For this evaluation different breast-cancer cell lines were used (Cama-1, MCF-7, MDA-MB231 and ZR-75-1), and real-time PCR was carried out for Cytokeratin 8, 18 and 19 as marker genes [2, 3]. Cancer cells were added in rising numbers and calibration curves could be drawn [Fig. 1], showing an increase in gene expression level from 10 cells added to a blood sample upwards, meaning that even a small number of cancer cells in the blood (resembling the ‘real’ conditions, with 1 CTC per106–7 surrounding blood cells) can be detected by this methodology.
PCR marker genes for CTC detection
As CTCs in the blood are rare, PCR marker genes have to be selected as accurately as possible. The first choice are the Cytokeratin (CK) genes 8, 18 and 19, as they are also used in the routinely applicated APAAP-staining, which is a histochemical detection method for CTCs. The cytokeratin family members are characteristic epithelial cell markers and only weakly expressed in blood cells, rendering them potentially useful for PCR-based detection of CTCs.
Three other genes (BCSP, MGL, Her2) were selected and used in an approach to detect differences in gene expression between normal individuals and adjuvant and metastatic breast-cancer patients [4]. Mammaglobin (MGL) is a gene which is only expressed in the adult mammary gland and is known to be upregulated in breast cancer [5]. Breast cancer specific protein (BCSP) is highly expressed in advanced infiltrating breast cancer and is a marker for recurrence of the disease and formation of metastases [6], and c-erbB2 (Her2) was used, because it is over-expressed in 20% of breast cancers and is also responsible for the aggressiveness of the tumour [7].
These markers were comparatively analysed in blood samples withdrawn from adjuvant and metastatic breast-cancer patients during surgery. The gene expression levels of adjuvant as well as metastatic breast-cancer patients were normalized to levels in blood samples from 20 healthy donors, considered as a negative control group. Differences in gene expression between the three sample groups were detected [Fig. 2] and it was attempted to find a signature of marker genes for CTCs in breast cancer by real-time PCR.
From the experiments, it could be concluded that cytokeratin genes seem to be the most promising markers for the detection of CTCs from peripheral blood of breast-cancer patients with reverse-transcription real-time PCR. The most suitable marker of the cytokeratin array is apparently CK8, rendering most expression values >1.
MGL, BCSP, and Her2 mRNA show few expression values >1 as well in adjuvant as in metastatic patients. Altogether, higher amplitudes for these three genes were detected in the adjuvant setting. CTCs can be detected from peripheral blood by real-time-PCR, using the cytokeratin markers, especially cytokeratin 8.
In contrast to these findings are the results published by Obermayr et al. 2010 [8], who found an overexpression of MGL/hMAM in 39% of the examined advanced breast cancer cases. But they also conclude that using more marker genes for CTC detection results in a higher percentage of detected cancer cases. The same findings were obtained by [9], who also used a real-time PCR-based approach for CTC detection. They used CK19, SCGB2A2, MUC1, EPCAM, BIRC5 and Her-2 as marker genes and found a high sensitivity and specificity (56.3% and 100% respectively).
Additionally CK20 was identified as a promising marker gene [10] and seems to be correlated with the aggressiveness of the tumour. To further improve the detection of CTCs by real-time-PCR, more marker genes need to be tested; promising candidates are, for example, MMP13 [11], UBE2Q2 [12],
Nectin-4 [13], and ALDH [14].
Future directions for cancer therapy
Real-time PCR-based techniques were already used for solid tumour profiling and are considered to be objective, robust and cost-effective molecular techniques that could be used in routine cancer diagnosis. In future, a real-time PCR assay for the detection of circulating tumour cells from peripheral blood could find its way into modern medicine. This would be advantageous for the patient by limiting the number of invasive procedures, such as biopsies or bone marrow aspirations, that have to be undertaken to produce samples for analysis.
Furthermore by implication of more marker genes a characterization of tumour cells could be pursued, which already gives hints towards a cancer prognosis, as for example Bölke et al. described, that the expression of certain genes is correlated to advanced breast cancer stages [15]. A better knowledge of cancer properties in turn will help to apply a more personalized therapy, side effects can be reduced and treatment efficiency will strongly increase.
References
1. Livak KJ, Schmittgen TD. Methods 2001; 25(4): 402–408.
2. Zebisch M, Kolbl AC, Schindlbeck C, Neugebauer J, Heublein S, Ilmer M, Rack B, Friese K, Jeschke U, Andergassen U. Anticancer Res 2012; 32(12): 5387–5391.
3. Zebisch M, Kölbl AC, Andergassen U, Hutter S, Neugebauer J, Engelstädter V, Günthner-Biller M, Jeschke U, Friese K. Biomedical reports; accepted for publication 2012.
4. Andergassen U, Hofmann S, Kolbl AC, Schindlbeck C, Neugebauer J, Hutter S, Engelstadter V, Ilmer M, Friese K, Jeschke U. Int J Mol Sci 2013; 14(1): 1093–1104.
5. Fleming TP, Watson MA. Ann N Y Acad Sci 2000; 923: 78–89.
6. Wu K, Weng Z, Tao Q, Lin G, et al. Cancer Epidemiol Biomarkers Prev 2003; 12(9): 920–925.
7. Kim YS, Konoplev SN, Montemurro F, Hoy E, Smith TL, et al. Clin Cancer Res 2001; 7(12):4008–4012.
8. Obermayr E, Sanchez-Cabo F, Tea MK, Singer CF, Krainer M, et al. BMC Cancer 2010; 10: 666.
9. de Albuquerque A, Kaul S, Breier G, Krabisch P, Fersis N. Breast Care (Basel) 2012; 7(1): 7–12.
10. Tunca B, Egeli U, Cecener G, Tezcan G, Gokgoz S, Tasdelen I, et al. Tumori 2012; 98(2): 243–251.
11. Chang HJ, Yang MJ, Yang YH, Hou MF, Hsueh EJ, Lin SR. Oncol Rep 2009; 22(5): 1119–1127.
12. Nikseresht M, Seghatoleslam A, Monabati A, et al. Cancer Genet Cytogenet 2010; 197(2): 101–106.
13. Fabre-Lafay S, Garrido-Urbani S, Reymond N, et al. J Biol Chem 2005; 280(20): 19543–19550.
14. Dontu G. Breast Cancer Res 2008; 10(5): 110.
15. Bolke E, Orth K, Gerber PA, Lammering G, Mota R, et al. Eur J Med Res 2009; 14(8): 359–363.
The authors
Ulrich Andergassen* MD, Alexandra C. Kölbl PhD, Klaus Friese MD, and Udo Jeschke PhD
Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe, Ludwig Maximilian University of Munich, Munich, Germany
*Corresponding author
ulrich.andergassen@med.uni-muenchen.de
Tissue biomarkers of breast cancer: implications for prognosis
, /in Featured Articles /by 3wmediaBetter tissue biomarkers are needed to improve diagnosis and prognosis, guide molecularly targeted therapy, and monitor activity and therapeutic response across many cancers. Proteomics methods, based on mass spectrometry, hold great promise for the discovery of novel biomarkers that might form the foundation of a new clinical test. This review will focus on potential tissue biomarkers with utility for prognosis in breast cancer.
By Dr Liping Chung
Tissue biomarkers in breast cancer
Breast cancer is the leading cause of mortality among women worldwide. It is a complex and heterogeneous disease and includes several subtypes, which have different prognoses and responses to therapy. Recent molecular characterization of some breast cancer subtypes has led to the development of personalized options for treatment targeting [1].
One of the major advantages of biomarker research for individuals with cancer is the availability of tumour tissue for analysis and the possibility that potential tissue biomarkers can be detected in histological samples. In conjunction with tumour grading and measurement of lymphovascular invasion, several tissue biomarkers are now used with prognostic significance in daily practice including estrogen receptor (ER), progesterone receptor (PR), the type 2 epidermal growth factor receptor (HER2 or erbB-2), and Ki67 [1, 2].
The identification of protein biomarkers in easily accessible biological fluids has potential for the development of minimally invasive procedures for early diagnostics, but the analysis of body fluids such as plasma, serum and urine is complicated by their wide dynamic range of protein expression, the variation in their composition and their sensitivity to sample handling. Many serum biomarkers are not very specific or sensitive [1]. Analysis of tissue homogenates using the well-established and extremely powerful conventional techniques of differential proteomics has the advantage of covering the lower range of protein expression in such samples than in biological fluids [3].
Prognosis and response prediction
Different from diagnostic markers that detect the potential for developing a malignancy or test for the presence of a malignancy, biological markers that predict prognosis once a cancer has occurred are of great importance because they may influence major therapeutic recommendations. For breast cancer, these markers have become part of contemporary clinical practice. Among established tissue marker proteins in breast cancer, ER and HER2 are not diagnostic but have the greatest predictive utility [2]. It is generally accepted that estrogen receptor-positive (ER+) and ER-negative (ER−) breast cancers represent different disease entities. ER- tumours tend to be of high grade, have more frequent p53 mutations, and have worse prognosis compared with ER+ disease. Both ER+ and ER- tumours can be either HER2 positive or negative. Low-grade tumours are typically ER positive, and almost always HER2 non-amplified. The approximately 15% of patients with breast cancer who have HER2 overexpressing and amplified tumours are typically treated with a combination of trastuzumab, a monoclonal antibody targeting HER2, and adjuvant chemotherapy [4]. HER2 amplification and overexpression are generally associated with a poor prognosis. The prognostic significance of HER2 overexpression in tumour tissue has been evaluated in several clinical trials, suggesting that HER2 positivity is correlated with worse prognosis in untreated breast cancer patients, including node-negative populations [5].
The search for breast tissue biomarkers by mass spectrometry-based proteomics
Proteomic approaches, particularly those involving mass spectrometry (MS), have been widely used in breast cancer biomarker discovery, although to date no new markers based on proteomic discovery have been adopted for use in clinical practice. Using laser capture microdissection (LCM) for tissue samples, an extensive tissue study was performed by MALDI-MS (matrix-assisted laser desorption/ionization mass spectrometry) analysis on an average of 2000 cells from 122 invasive mammary carcinomas and 167 samples of normal breast epithelium [6]. Among clusters of protein/peptide peaks that were used to discriminate cancer from normal tissue with high sensitivity and specificity were ubiquitin, S100A6 (calcyclin) and S100A8 (calgranulin A). To confirm cDNA expression profiling of breast tissues, Brozkova et al. also analysed whole tissue lysates rather than serum of 105 breast carcinomas on IMAC30 protein chips by SELDI-TOF MS (surface-enhanced laser desorption/ionization, time-of-flight mass spectrometry) [7]. They compared this analysis to cDNA expression profiling of the same tumours and found similar clustering, providing supporting evidence for the effectiveness of this technique in identifying and classifying tumours.
Most clinical tissue samples are conserved as formalin-fixed paraffin-embedded (FFPE) samples. In particular, cancer tissues contain several different cell types at various developmental stages. It was generally believed that proteins in FFPE tissues were altered and inaccessible for analysis by mass spectrometry until recent developments have shown it is possible to access the protein in imaging mass spectrometry (IMS) experiments following antigen retrieval [8]. The direct analysis of cancer tissues by IMS preserves the spatial proteomic information. Consequently, it is holds great promise for the discovery of highly specific biomarkers. A recent study demonstrated the potential of MALDI-imaging MS for HER2 status of clinical parameters in cases of breast cancer based on protein patterns. This potentially allows the selection of patients likely to respond to trastuzumab treatment. Comparing the HER2-positive (HER2+) vs HER2-negative (HER2−) breast cancer protein profiles, the authors found a specific proteomic signature of seven species, able to accurately classify the HER2 status with a sensitivity of 83%, a specificity of 92% and an overall accuracy of 89% [9].
Protein biomarkers and conventional pathologic features
In a very recent study, using protein extracts of breast tissues (n=171), we have used SELDI-TOF MS to discover two proteins that, in combination, show high discrimination between breast cancer and healthy breast tissue samples [10]. These putative breast cancer biomarkers were verified on an independent sample set, and identified as ubiquitin and a novel truncated form of the S100 protein family member, S100P. Interestingly, the combined panel of two protein markers was significantly associated with tumour histologic grade, size, and lymphovascular invasion (LVI), and also with ER-positive (ER+) and PR-positive (PR+) status and HER2 overexpression. In particular, as shown in Figure 1, significant positive associations were seen between a previously unreported short form of S100P (9.2kDa) and tumour size, high grade, LVI and lymph node involvement (LN), and also associated with hormone receptor positive status and HER2 overexpression (unpublished data). These results implicate that a protein biomarker panel may indicate a HER2-enriched breast cancer subtype with poor prognosis, and that measurement of S100P may be valuable both in the classification of breast cancer and as a possible target for treatment. Furthermore, in another very recent study, the prognostic value of S100P was also tested for FFPE tissue obtained from 85 breast cancer patients with a median follow up of 17 years. High immunocytochemical staining of breast tumour sections for S100P has been associated with poor long-term patient survival [11].
Conclusion and future prospects
In this era of using new high-throughput methods, many new protein biomarkers have been reported for both prognostic and predictive purposes. However, none of these have been widely accepted in routine clinical practice, possibly due to a lack of sufficient validation to meet the criteria of the American Society of Clinical Oncology’s tumour marker utility grading system and guideline recommendations [1]. Identification of novel markers based on gene expression and proteomic profiling has led to more definitive insights into tumour biology. The accurate evaluation of the status of clinical parameters in cases of breast cancer is of primary importance for prognostic value and therapeutic decision. Different methodologies successfully used for breast cancer prognostic information and therapy outcome prediction may suggest that the future diagnostics and consequent individualization of therapy will become much more wide-ranging.
References
1. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, Somerfield MR, Hayes DF, Bast RC, Jr. American Society of Clinical Oncology 2007 update of recommendations for the use of tumour markers in breast cancer. J Clin Oncol 2007; 25(33): 5287–5312.
2. Chung L, Baxter RC. Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays. Expert Rev Proteomics 2012; 9(6): 599–614.
3. Danova M, Delfanti S, Manzoni M, Mariucci S. Tissue and soluble biomarkers in breast cancer and their applications: ready to use? Journal of the National Cancer Institute Monographs 2011; 2011(43): 75–78.
4. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, Watson M, Davies S, Bernard PS, Parker JS, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. Journal of the National Cancer Institute 2009; 101(10): 736–750.
5. Andrulis IL, Bull SB, Blackstein ME, Sutherland D, Mak C, Sidlofsky S, Pritzker KP, Hartwick RW, Hanna W, Lickley L, et al. neu/erbB-2 amplification identifies a poor-prognosis group of women with node-negative breast cancer. Toronto Breast Cancer Study Group. J Clin Oncol 1998; 16(4): 1340–1349.
6. Sanders ME, Dias EC, Xu BJ, Mobley JA, Billheimer D, Roder H, Grigorieva J, Dowsett M, Arteaga CL, Caprioli RM. Differentiating proteomic biomarkers in breast cancer by laser capture microdissection and MALDI MS. J Proteome Res 2008; 7(4): 1500–1507.
7. Brozkova K, Budinska E, Bouchal P, Hernychova L, Knoflickova D, Valik D, Vyzula R, Vojtesek B, Nenutil R. Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression. Breast Cancer Res 2008; 10(3): R48.
8. Gustafsson JO, Oehler MK, McColl SR, Hoffmann P. Citric acid antigen retrieval (CAAR) for tryptic peptide imaging directly on archived formalin-fixed paraffin-embedded tissue. J Proteome Res 2010; 9(9): 4315–4328.
9. Rauser S, Marquardt C, Balluff B, Deininger SO, Albers C, Belau E, Hartmer R, Suckau D, Specht K, Ebert MP, et al. Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry. J Proteome Res 2010; 9(4): 1854–1863.
10. Chung L, Shibli S, Moore K, Elder EE, Boyle FM, Marsh DJ, Baxter RC. Tissue biomarkers of breast cancer and their association with conventional pathologic features. Br J Cancer 2013; 108(2): 351–360.
11. Maciejczyk A, Lacko A, Ekiert M, Jagoda E, Wysocka T, Matkowski R, Halon A, Gyorffy B, Lage H, Surowiak P. Elevated nuclear S100P expression is associated with poor survival in early breast cancer patients. Histol Histopathol 2013; 28(4): 513–524.
The author
Liping Chung PhD
Kolling Institute of Medical Research,
University of Sydney, Royal North Shore Hospital, NSW 2065, Australia
E-mail: liping.chung@sydney.edu.au
Genetic diagnostics in pediatric hearing loss
, /in Featured Articles /by 3wmediaHearing impairment in newborn children is one of the most frequent forms of sensorineural disorders, affecting 1 in 1000 infants. In half of the cases the hearing loss has a genetic basis, and over 70 genes have been identified so far, making hearing loss genetically exceptionally heterogeneous. Early detection in newborns, in combination with a genetic diagnosis is critical for the selection of a proper intervention and the development of speech, language and communication skills.
by Dr Isabelle Schrauwen
Hearing impairment in infants can be due to environmental influences such as cytomegalovirus infection, but in industrialized countries, however, most cases of early-onset hearing impairment have a genetic basis. Genetic hearing loss is non-syndromic in 70% of cases, whereas other symptoms (apart from hearing loss) are noticeable in 30% of cases (syndromic hearing loss). Autosomal recessive non-syndromic hearing loss (ARNSHL) is most common (80%) and is typically prelingual in onset, and autosomal dominant non-syndromic hearing loss (ADNSHL), X-linked and mitochondrial hearing loss are less frequent (20 and <1% respectively). To date, over 70 genes have been found to be implicated in non-syndromic hearing loss (NSHL), of which 40 are autosomal recessive. The most frequent causes of ARNSHL in most populations are mutations in GJB2, with a frequency ranging from 10 to 50% of all ARNSHL cases.
The implementation of newborn hearing screening in many countries has lead to an early detection of hearing loss and deafness in infants. This, together with improved genetic diagnostics and neuroimaging, has lead to a better understanding and better intervention of hearing loss overall [1].
The importance of a genetic diagnosis in pediatric hearing impairment
Clinical tests are not always sufficient for an accurate diagnosis and genetic diagnostics can provide answers that clinical tests cannot. Identification of the genetic cause can help predict the progression of the hearing loss and also direct the choice of the most appropriate treatment or method of communication. In addition, some apparent forms of non-syndromic hearing loss can be diagnosed to be syndromic as they give other symptoms at a later age (such as goitre in Pendred syndrome or retinitis pigmentosa in Usher syndrome). For Usher syndrome, preventative measures can be taken including sunlight protection and vitamin therapy to minimize the rate of progression of retinitis pigmentosa [2]. Furthermore, autosomal recessive mutations in GJB2 often cause a stable form of hearing loss and patients usually have good prospects with a cochlear implant. Knowing the gene responsible can also be very important to the parents, reducing their feelings of guilt and predicting the likelihood of subsequent children having hearing loss.
In addition, more extensive screening will also be very useful in providing a more accurate picture of the prevalence of different types of deafness affecting people across the world. Finally, advances in molecular and cellular therapies for hearing loss are also gene-specific [3], and identification of the genetic cause is key.
Gene-specific sequencing
Until recently, routine molecular diagnostics for hearing impairment consisted of the gene-specific sequencing of certain deafness genes, mainly with Sanger sequencing. GJB2 testing is offered most frequently in routine diagnostics, as it is responsible for a large number of ARNSHL cases. When there is evidence of progression of the hearing loss, or the presence of a goitre, an enlarged vestibular aqueduct (EVA), or Mondini dysplasia, SLC26A4 will be analysed, and when a specific phenotype is seen, other genes might also be analysed (OTOF, TECTA, COCH, WFS1, or a mitochondrial mutation). The selection criteria are typically: (1) high frequency cause of deafness (i.e. GJB2); (2) association with another recognizable feature (i.e. SLC26A4 and EVA); or (3) a recognizable
audioprofile (i.e. WFS1) [4].
Syndromic forms of deafness usually only have one or a few candidate genes responsible for each syndrome. However, for non-syndromic deafness, it is very difficult, and often impossible, to determine candidate genes because of the large number of causative genes leading to a relatively indistinguishable phenotype. GJB2 sequencing will identify 10–50% of ARNSHL cases, but the remaining cases of hearing loss display a high degree of genetic heterogeneity and unless a specific audioprofile is present it is hard to diagnose these with a gene-specific test. Traditionally, with gene-specific tests, it has therefore been difficult to establish a genetic diagnosis due to extreme genetic heterogeneity and a lack of phenotypic variability.
Microarrays
The analysis of multiple mutations in several genes in parallel was made possible by the development of single nucleotide extension microarrays [5]. These microarrays detect a specific mutation by hybridizing primers to patient DNA, followed by a single base extension. This technology therefore only detects known mutations, and a panel of 198 mutations in 8 genes [GJB2, GJB6, GJB3, GJA1, SLC26A4, SLC26A5 and the mitochondrial genes encoding 12S rRNA and tRNA-Ser(UCN)] underlying sensorineural (mostly non-syndromic) hearing loss has been developed [5]. Although new mutations cannot be picked up, this technique can provide some additional diagnostic value in GJB2 negative cases.
An Affymetrix resequencing microarray capable of resequencing 13 genes mutated in NSHL was also developed (GJB2, GJB6, CDH23, KCNE1, KCNQ1, MYO7A, OTOF, PDS, MYO6, SLC26A5, TMIE, TMPRSS3, USH1C) [6], but the number of genes here is also limited and specific kinds of mutations such as insertion/deletion (indel) mutations cannot be detected accurately.
Custom gene enrichment with next-generation sequencing
The need for new and better diagnostic methods for extremely heterogeneous diseases has been filled by the availability of next-generation sequencing, which has made it possible to sequence a large number of genes at the same time. This has lead to an immense growth of custom hearing-loss gene panels. Several labs have adopted this approach in-house already [7–9], and several labs offer this test for ARNSHL, ADNSHL, some cases of syndromic hearing loss, or all of the above.
The most commonly available systems for massive parallel sequencing are: Illumina, 454, or SOLiD. The Illumina platform is the most widely used platform to date and relies on cyclic reversible termination technology. Before massive parallel sequencing, DNA will be enriched for a custom selection of hearing-loss genes. In a diagnostic setting, sensitivity and specificity are important, and different enrichment methods perform differently in these criteria. Capture enrichment methods have been used more often and are easy to use, but PCR-based methods seem to have a better performance. A portion of targeted bases in repetitive regions cannot be captured, whereas PCR is able to enrich 100% of the desired target area. This is crucial to the sensitivity of detecting variants.
Although PCR-based techniques are usually more labour-intensive, microdroplet PCR methods have improved this greatly [9]. By using barcoding, custom hearing-loss panels are now offered for a competitive price in several labs across the world, and depending on the genes included in the panel, will offer a genetic diagnosis in the majority of cases.
Exome sequencing
Exome sequencing is also emerging as a diagnostic tool for many diseases and has decreased in price significantly in recent years. Exome sequencing targets every coding exon in the genome for enrichment prior to next-generation sequencing. Though current exome kits provide insufficient target enrichment in a diagnostic setting for deafness [9], as the regions of interest might not been completely covered and coverage depth may not be high enough for a diagnostic setting. Exome sequencing has therefore a decreased sensitivity to detect mutations in known genes compared to the custom panels available, but does allow the identification of new genes. In addition, given the amount of data that arises from exome sequencing, identification of the causative mutation among the list of variants will be more challenging. Although over 70 genes have already been discovered, there are still many more to be found, and the identification of new genes will greatly improve our understanding of deafness. Since its introduction, exome sequencing has lead to a fast rise in the identification of hearing-loss-related genes.
Future techniques and conclusions
Other technologies, such as Ion torrent, Pacific Biosystems, and specifically the emerging Oxford Nanopore technique, might offer very cost-effective sequencing methods for the future of molecular diagnostics in many diseases. Furthermore, genome sequencing might be shown useful in the diagnosis of hearing loss if the price of sequencing keeps dropping.
In conclusion, a genetic test ideally has to be sensitive, specific, accurate and low in cost. Gene-specific analysis of GJB2 will detect a 10–40% of ARNSHL cases, and custom gene panels with next-generation sequencing will provide a diagnosis in the majority of genetic hearing-loss cases. It is anticipated that within the coming years genetic testing will be routinely implemented in pediatric hearing loss, leading to better intervention and choice of treatment.
References
1. Paludetti G, et al. Infant hearing loss: from diagnosis to therapy Official Report of XXI Conference of Italian Society of Pediatric Otorhinolaryngology. Acta Otorhinolaryngol Ital 2012; 32: 347–70.
2. Hamel C. Retinitis pigmentosa. Orphanet J Rare Dis 2006; 1: 40.
3. Hildebrand MS, et al. Advances in molecular and cellular therapies for hearing loss. Mol Ther 2008; 16: 224–36.
4. Hilgert N, et al. Forty-six genes causing nonsyndromic hearing impairment: which ones should be analyzed in DNA diagnostics? Mutation Res 2009; 681: 189–96.
5. Gardner P, et al. Simultaneous multigene mutation detection in patients with sensorineural hearing loss through a novel diagnostic microarray: a new approach for newborn screening follow-up. Pediatrics 2006; 118: 985–94.
6. Kothiyal P, et al. High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays. BMC Biotechnol 2010; 10: 10.
7. Shearer AE, et al. Comprehensive genetic testing for hereditary hearing loss using massively parallel sequencing. Proc Natl Acad Sci U S A 2010; 107: 21104–9.
8. Brownstein Z, et al. Targeted genomic capture and massively parallel sequencing to identify genes for hereditary hearing loss in Middle Eastern families. Genome Biol 2011; 12: R89.
9. Schrauwen I, et al. (2013) A sensitive and specific diagnostic test for hearing loss using a microdroplet PCR-based approach and next generation sequencing. Am J Med Genet A 2013; 161A: 145–52.
The author
Isabelle Schrauwen PhD 1,2
1 Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
2 The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
E-mail: isabelle.schrauwen@ua.ac.be
Molecular techniques used in the diagnosis of cutaneous lymphoma
, /in Featured Articles /by 3wmediaCutaneous lymphomas are a heterogenic group of conditions often difficult to diagnose. The diagnosis requires careful correlation between clinical presentation pathology and molecular analysis. Molecular analysis includes inmunophenotyping, clonality assays and rarely chromosomal analysis. The importance of molecular analysis hinges on two main reasons: firstly to confirm the diagnosis and secondly to further characterize the nature of the lymphoma. In addition, molecular analysis may provide some further insight on the origin of the malignancy, for example if it is primarily cutaneous or if the skin is a secondary site of involvement.
by Dr Belén Rubio González and Dr Joan Guitart
Recently, cancer has been defined by unlimited growth of cells derived from a single mutated cell or a clonality expansion. The detection of a monoclonal population may help to distinguish a lymphoma from a reactive process. However, on the one hand, clonality by itself does not imply malignancy and, on the other hand, a negative clonality result does not rule out a malignant condition. During this process, genes encoding the antigen receptor immunoglobulin (Ig) for B cells and the T-cell receptor (TCR) for T cells are rearranged as commonly seen in primed lymphocytes, resulting in a wide diversity of unique antigen receptors providing high antigenic specificity.
The clonal nature of several skin conditions may help us recognize pre-malignant stages or the concept of cutaneous lymphoid dyscrasias (CLD) which has been recently introduced and includes parapsoriasis, pigmented purpuric dermatosis, idiopathic follicular mucinosis, pityriasis lichenoides, syringolymphoid hyperplasia with alopecia, and idiopathic generalized erythroderma (pre-Sézary). Although almost all of these conditions never progress to a frank malignancy, they have the potential risk of converting into cutaneous T-cell lymphoma (CTCL). The recognition of a T-cell clone may identify these dermatoses, which have been difficult to categorize in the past.
Clonality methods
T-cell clonality studies are based on the detection of specific T-cell receptor gene rearrangements (TCR-GR) by Southern blot analysis (SBA) or polymerase chain reaction (PCR). We should expect that the tumour cells contain identical TCR-GRs, reflecting a monoclonal T-cell population.
SBA used to be the gold standard for detection of T-cell clonality, but the procedure is laborious and lengthy. Furthermore, fresh or frozen tissue and radioactive probes are required. If this method is used, the clonal population must represent at least 5% of the total DNA extracted, which includes cells other than T-cells decreasing the sensitivity of the test. For the reasons above, SBA has been gradually replaced by PCR techniques.
The overall sensitivity of PCR-based methods for detection of T cell clonality ranges between 70 and 90%, with specificity range depending on the sample population. The test amplifies extracted DNA using primers directed against the TCR beta, gamma and delta chains. The gamma chain gene is most commonly used because of the lower complexity of the gene. Adding probes to the beta TCR gene allows for a higher sensitivity and specificity of the clonal analysis.
In the case of B-cells, PCR uses primers for four conserved reliable targeted regions for immunoglobulin heavy-chain. In our experience the sensitivity of PCR for the immunoglobulin heavy chain is lower than for T-cell clonality. The detection of light chain restriction by immunophenotypic test (often referred as monotypical immunoglobulin expression) is also consistent with a clonal B-cell population. This can be accomplished with immunohistochemistry at the protein level or in situ hybridization at the RNA level. Monoclonality can also be demonstrated with flow cytometry targeting kappa and lambda light chain expression at the B-cell membrane. An international consensus on B- and T-cell clonality assays was established with the BIOMED-1 proposal.
In most of the conventional PCR methods monoclonality is defined by the presence of a band after high-resolution capillary gel electrophoresis of the PCR product. Using temperature- or chemical-gradient gel electrophoresis can enhance separation of DNA products. After that, fluorescent fragment analysis using consensus primers for the TCR gene and the fluorescence input is analysed by capillaroscopy. Furthermore, clonal definition should be confirmed using multiple PCR probes labelled with different fluorochromes.
The detection of a dominant T-cell clone, defined as the same PCR product at different sites (two skin biopsies, skin and blood, skin and lymph node, etc.) implies dissemination of a prevailing T-cell clone, and has been associated with a higher incidence of tumour progression. Clonal heterogeneity has been reported in patients with early stage or indolent mycosis fungoides (MF) and in CLD conditions without a malignant process.
The value of the detection of circulating clonal T-cells in peripheral blood has been debated. That is much more common in patients with erythrodermic MF (42%) compared to other lower stages (12.5%). It may also help in distinguishing a dominant CTCL clone from innocent cytotoxic T-cell clones, which are often detected in the blood of elderly patients.
In the context of palpable lymphadenopathy, detection of the same clone in the lymph node and the skin CTCL lesions may indicate a poor prognosis, similar to the identification of lymphoma by histology.
T-cell clonality and significance
TCR clonality should be tested for in skin and blood samples at the time of diagnosis when a cutaneous lymphoma is suspected. The detection of a dominant clone in both sites is important to confirm the diagnosis and for prognostic guidance. T-cell clonality is particularly helpful in the early stage of an MF which does not include sufficient clinical or microscopic evidence for the diagnosis. TCR gamma clonality was positive in 53% of the patch stage and in 100% of plaque or tumour stage in different series. An increased rate of clonality was observed in connection with more advanced cutaneous disease and higher histopathological diagnostic score.
False-positive monoclonal and oligoclonal bands may be identified in inflammatory dermatosis, where the T-cell infiltrate is sparse. Amplification of TCR-GRs from a few T-cells may result in a false-positive clone or ‘pseudomonoclonality’. A pseudoclone is infrequently associated with a malignant T-cell process. Repeating the analysis using the same DNA template or fresh DNA extraction may solve the problem because in reactive conditions, the predominant PCR product typically varies in repeated analysis of the same sample. In contrast, in lymphomas, dominant TCR clones are reproducible and should be routinely verified to confirm monoclonality.
A correlation between TCR clonality by PCR methods and response to treatment has been suggested in several studies. The absence of a detectable clone in CTCL was associated with a higher rate of complete remission, but was not necessarily associated with improved survival.
Also immunophenotypic and immunogenotypic assays have been used to monitor the response of CTCL to therapy. The concept of minimal residual disease is defined as the persistence of the tumour T-cell clone in tissue or blood despite clinical complete remission status. Minimal residual disease as detected by deep sequencing methods may help identify patients at risk of relapse but the real prognosis is still uncertain. In the future, the presence or absence of the dominant or persistent clone may guide our therapeutic approach, aiming for more durable remissions while minimizing the adverse effects of therapy.
Other methods used in the olecular diagnosis of cutaneous lymphomas
Flow cytometry analysis
Blood flow cytometry analysis (FCA) is routinely performed in erythrodermic patients to rule out Sézary syndrome (SS). This method is based on the abnormal expression of various surface markers of malignant T-cells compared with normal T-cells. Other helpful findings are the demonstration of overwhelming dominance of specific T-cell subsets (clusters of differentiation CD4 vs CD8) and the loss of one or more pan-T-cell antigens (i.e. CD2, CD3, CD5, and CD7). A high CD4 : CD8 ratio of more than 10 : 1 and loss of CD7 and CD26 are the most reliable findings in SS. However, low CD7 expression has lower specificity because some inflammatory diseases also show the same deletion. The addition of CD26 to standard T-cell panels enhances the sensitivity of FCA in the diagnosis of SS.
Moreover, flow cytometry is able to detect a clonal population by using antibodies against different subsets of T-lymphocytes based on the expression of V beta family antibodies. This is used mainly as a research tool because the extensive panel of antibodies is expensive, incomplete and does not include the entire spectrum of V beta families.
Fluorescence in situ hybridization
Fluorescence in situ hybridization (FISH) involves annealing of fluorescently labelled nucleic acid probes with complementary DNA or RNA sequences and the subsequent detection of these probes within fixed cells. FISH is used to detect major chromosomal gains or losses, as well as specific translocations, and requires a target specific probe. Although FISH is not routinely used in the diagnosis of cutaneous lymphomas, recent publications have shown its potential for future applications in various areas.
Genomic analysis by microarray assays or comparative genomic hybridization
Comparative genomic hybridization (CGH) allows the identification of chromosomal imbalances but it is not able to identify specific genes involved due to its measurement resolution. The microarray-based CGH is more precise, and chromosomal imbalances can be quantified and defined appositionally. A high frequency of gains in chromosomes 1, 7, 8, and 17 and losses of chromosomes 5, 9, and 13 was demonstrated using array-based CGH for identification of genomic differences between SS and MF.
Conclusion
Molecular diagnosis, in combination with a meaningful correlation with histological results and clinical presentations can provide an important tool in the evaluation of cutaneous lymphoid infiltrate. While PCR-based clonality techniques need to be interpreted with caution, modern capillaroscopy methods offer clone-specific data that allow us to improve the accuracy for diagnosis, prognosis and staging implication.
References
1. Deonizio JM, Guitart J. Semin Cutan Med Surg 2012; 31: 234–240.
2. Groenen PJ, Langerak AW, et al. J Hematop 2008; 1: 97–109.
3. Guitart J, Magro C. Arch Dermatol 2007; 143: 921–932.
4. Rübben A, Kempf W, et al. Exp Dermatol 2004; 13: 472–483.
5. Kulow BF, Cualing H, Steele P, et al. J Cutan Med Surg 2002; 6: 519–528.
6. Nihal M, Mikkola D, et al. Hum Pathol 2003; 34: 617–622.
7. Meyerson HJ. G Ital Dermatol Venereol 2008; 143:21–41.
8. Van Dongen JJ, Lamgerak AW, et al. Leukemia 2003; 17: 2257–2317.
9. Sandberg Y, Heule F, et al. Haematologica 2003; 88: 659–670.
10. Guitart J, Camisa C, et al. J Am Acad Dermatol 2003; 48: 775–779.
The authors
Belén Rubio González* MD and Joan Guitart MD
Northwestern Medical Hospital, Chicago, IL, USA
*Corresponding author
E-mail: rubiogonzalezbelen@gmail.com
DNA microarrays for SNP profiling in thrombosis and hemochromatosis
, /in Featured Articles /by 3wmediaSpecialized diagnostic DNA microarrays provide fast and reliable determination of factor V and factor II gene mutations associated with thrombosis or HFE gene defects linked to hereditary hemochromatosis. The simple microarray procedure includes fully automated data analysis and can be performed on whole blood samples, circumventing the need for preanalytical DNA isolation. Patient genotyping aids diagnosis in symptomatic individuals and risk assessment in healthy individuals, thus facilitating decision making in therapy and prevention.
by Dr Jacqueline Gosink
Laboratory analysis of genetic determinants is gaining momentum as ever increasing numbers of disease-associated alleles are discovered. With cutting edge diagnostic microarray technology, newly identified DNA parameters can progress rapidly from the research laboratory to routine diagnostics. Microarray platforms such as the EUROArray provide quick and easy determination of DNA mutations, enriching diagnosis and risk evaluation in a range of genetically linked diseases.
This article focuses on DNA microarray systems for genetic analysis in two common hereditary hematological disorders. The first detects single nucleotide polymorphisms (SNPs) in the factor V (FV) and/or factor II (FII) genes that lead to thrombosis and embolism. The second identifies up to four SNPs in the HFE (high iron) gene that contribute to hereditary hemochromatosis.
Thrombosis and embolism
Deep and superficial venous thrombosis and thromboembolism of the brain, lung and coronary vessels are among the most frequent causes of death, especially in western industrialized countries. These conditions result from a combination of genetic susceptibility and exogenous factors such as old age, immobility, smoking, diabetes mellitus, pregnancy, oral contraceptives or hormone replacement therapy. Notably, more than half of all cases can be attributed to genetic factors, particularly if the disease occurs before the age of 45 without any obvious external factors or at an atypical location.
The most important and most frequent genetic risk factors are the FV Leiden 1691G>A mutation (APC resistance) and the FII 20210G>A mutation in the prothrombin gene. These DNA mutations result in amino acid substitutions which disrupt the blood coagulation functions of FV and FII.
Factor V and II mutations
In healthy individuals activated FV is normally prevented from triggering coagulation by proteolytic cleavage catalysed by activated protein C (APC) and its cofactor protein S. Persons with the FV Leiden mutation exhibit an altered form of FV resulting from an exchange of the amino acid at position 506 from arginine to glutamine. The modified structure of FV makes it resistant to inactivation by APC (APC resistance), which leads to hypercoagulability and an increased risk of thrombosis. More than 95% of cases of APC resistance are caused by the autosomal, dominant FV Leiden mutation. In Europe around 3-7% of the population is a heterozygous carrier. In these individuals the thrombosis risk is 3-8 times higher than in non-affected persons, and if oral contraceptives are taken up to 30 times higher. The homozygous FV Leiden mutation occurs in around 0.2% of the European population and is associated with a 50-100-fold increased risk of thrombosis.
The 20210G>A mutation in the FII gene leads to a raised plasma concentration of the coagulation factor prothrombin via an as yet unidentified mechanism. The resulting thrombosis can be venous or arterial. The heterozygous genotype is present in 1-3% of the population in Europe and is associated with a 3-fold higher risk of deep venous thrombosis. If oral contraceptives are taken, the risk of venous thrombosis is increased 16-fold and of brain venous thrombosis up to 150-fold.
The factor V and factor II gene defects have an additive effect, and thrombophilia patients who exhibit the FII 20210G>A mutation often also have the FV Leiden mutation. In these patients the risk of venous thrombosis is elevated by a factor of 20.
Genetic analysis of FV and FII mutations is of outstanding importance in individuals with a high thrombosis risk based on their personal or family history, as well as in patients with unexplained recurrent miscarriages, biochemically proven resistance to APC or proven protein C or protein S deficiency. Genetic risk determination should also be undertaken before prescribing oral contraceptives or hormone replacement therapy to women with a familial tendency to thrombosis, especially young smokers.
Hereditary hemochromatosis
Hereditary hemochromatosis is the most frequent autosomal, recessive inherited metabolic disorder and is characterized by increased resorption of iron in the upper small intestine. The augmented iron uptake leads to an increase in the total iron content in the body from around 2–6 g (normal value) to up to 80 g. Since the human body cannot excrete the excess iron, it is deposited in various organs such as the liver, pancreas, spleen, thyroid gland, pituitary gland, heart and joints. In untreated patients irreversible damage occurs, resulting in an increased risk of cardiomyopathy, arthropathy, diabetes mellitus, liver cirrhosis and liver and pancreas carcinoma. Most cases of hereditary hemochromatosis are caused by defects in the HFE gene, which lead to functional flaws in the encoded iron regulatory protein.
HFE mutations
There are four SNPs in the HFE gene that are associated with hereditary hemochromatosis. The two most frequent, representing 90% of cases, result in the amino acid substitutions C282Y or H63D which cause a loss or reduction of the physiological function of the Hfe protein. The penetrance of the mutations is dependent on age and gender. Thus, the disease does not necessarily manifest itself in all carriers of these mutations. The strongest disease association is observed in patients with a homozygous C282Y mutation, whereby the penetrance is much lower in young women than in men due to menstruation. While 80% of men under 40 with this gene defect develop hemochromatosis, less than 40% of women do so. The penetrance increases to 95% of men and 80% of women for the population group of over 40 year olds. The two further SNPs in the HFE gene that are associated with hereditary hemochromatosis are S65C, which results in an amino acid substitution in the Hfe protein, and E168X, which causes early termination of protein synthesis, whereby both of these mutations are rare.
Around 10% of the population in northern Europe is heterozygous for one of the disease-associated mutations in the HFE gene and 0.3–0.5% is homozygous. New studies show that 90–100% of hemochromatosis patients exhibit homozygous gene defects. However, even a mutation in one HFE allele is sufficient to cause at least minor abnormalities in iron metabolism. The early identification of HFE gene defects enables suitable preventative measures to be implemented, for example a reduction in the consumption of high-iron-containing foods.
Simple microarray analysis
DNA mutations associated with thrombosis and hemochromatosis can be reliably determined using DNA microarray systems such as EUROArray [1, 2]. This microarray system provides fast and efficient SNP detection with fully automated data analysis, and can easily be used by persons unfamiliar with molecular biology. A special feature of the thrombosis and hemochromatosis microarray procedures is the use of pretreated whole blood as sample material, which eliminates the need for a preanalytical DNA isolation step. The hands-on processing time for the direct procedure is thus reduced to as little as 1.5 minutes per sample.
In the microarray procedure [Figure 1], the sections of DNA containing the disease-associated alleles are amplified by multiplex polymerase chain reaction (PCR) using highly specific primers. During this process the PCR products are labelled with a fluorescent dye. The PCR mixture is then incubated with a microarray slide containing immobilized DNA probes [Figure 2]. The PCR products hybridize with their complimentary probes and are subsequently detected via the emission of fluorescence signals. The evaluation of the microarrays [Figure 3] proceeds quickly and objectively using the special microarray scanner and EUROArrayScan software. The software interprets the results, produces patient genotype reports, and archives all data and patient information [Figure 4]. It can be integrated seamlessly into existing laboratory software.
Reliable biochip technology
EUROArrays are based on proven biochip technology which has been adapted for DNA analysis. Each biochip is composed of DNA spots of wild type and mutant alleles and contains in addition integrated control sequences to verify correct performance of the test. The microarray slides are incubated using the established TITERPLANE technique, which provides standardized, parallel incubation of multiple samples. Up to five samples can be analysed per slide. The reproducibility and convenience of the analysis is further enhanced by ready-to-use PCR reagents and meticulously designed amplification primers and hybridisation probes. The entire procedure from sample arrival to report release is IVD validated and CE labelled.
In clinical evaluation using molecular genetically precharacterized samples, each microarray demonstrated a sensitivity of 100% and a specificity of 100% [Table 1]. Homozygous and heterozygous genotypes were reliably discriminated for every position.
Comprehensive microarray range
The thrombosis diagnostic microarray system is available in different constellations for separate or parallel analysis of the FV Leiden and FII 20210G>A mutations, while the hemochromatosis microarray system is available in two versions encompassing either just the two most frequent mutations C282Y and H63D or, for a more extensive analysis, the four disease-associated mutations C282Y, H63D, S65C and E168X.
In addition to the determination of FV/FII and HFE mutations, EUROArray technology can also be used to analyse further genetic risk factors such as HLA-DQ2/ DQ8 in celiac disease, HLA-Cw6 in psoriasis or HLA-B27 in ankylosing spondylitis. New parameters soon to be added to the platform include HLA-DR Shared Epitope in the diagnosis of rheumatoid arthritis and human papilloma virus detection and subtyping.
Summary
The current pace of genetic discoveries combined with advances in microarray technology is resulting in a plethora of novel DNA tests for the routine diagnostic laboratory. New DNA microarrays for rapid identification of thrombosis-associated mutations in the factor V/factor II genes and hemochromatosis-linked mutations in the HFE gene have greatly enhanced diagnosis and risk evaluation in susceptible individuals. Early awareness of a genetic predisposition enables individuals to adopt appropriate lifestyle or medical interventions to reduce the impact or even prevent development of these debilitating diseases.
References
1. Voss J. et al. to be presented at IFCC EuroMedLab, Milano, Italy (2013).
2. Axel K. et al. to be presented at IFCC EuroMedLab, Milano, Italy (2013).
The author
Jacqueline Gosink PhD
Euroimmun AG
Luebeck, Germany
RND efflux pumps in P. aeruginosa: an underestimated resistance mechanism
, /in Featured Articles /by 3wmediaAn adequate initial antibiotic therapy is a key determinant of therapeutic success in Pseudomonas aeruginosa – infected patients. Antibiotic efflux is an underestimated resistance mechanism because it may occur in strains categorized as susceptible. It is rarely or not at all diagnosed in routine laboratories and often masked by high-level resistance mechanisms.
by Dr Laetitia Avrain, Dr Pascal Mertens and Professor Françoise Van Bambeke
P. aeruginosa: state of the art of antibiotic susceptibility
P. aeruginosa is a Gram-negative bacterium recognized as a major cause of infections in hospitalized patients or in patients with impaired defences as observed in burn wounds or cystic fibrosis. In spite of improved hygiene measures, the risk of infection by P. aeruginosa in ICU remains high (infection incidence > 30/100 patients hospitalized in ICU). P. aeruginosa infections are associated with mortality rates as high as 30 % to 50 % in bacteremia [1] and up to 70% in patients with nosocomial pneumonia [2].
Yet, empirical selection of antibiotics is made difficult by the continuously evolving resistance of P. aeruginosa to antibiotics, notably due to the emergence of Multi Drug Resistance (MDR) phenotype (R ≥ 3 antibiotic classes). The MDR status of the strain as well as an initial inappropriate treatment negatively influence patient outcome [3].
Acquired high level resistance is due to the acquisition of genes coding for aminoglycoside-modifying enzymes or beta-lactamases, or to mutations in fluoroquinolone targets. Intrinsic antibiotic resistance is due to low outer membrane permeability mediated either by under production of the oprD porin, or by the expression of multidrug resistance efflux pumps. The genome of P. aeruginosa codes for numerous efflux pumps, among which MexAB-OprM and MexXY-oprM are of first clinical importance due to their large prevalence in clinical strains and their ability to expel several classes of chemically-unrelated antibiotics.
RND efflux pumps in P. aeruginosa
The main efflux pumps in P. aeruginosa belong to the Resistance-Nodulation-Division (RND) superfamily, which uses proton motive force as energy source. They constitute a tri-partite system, composed of an integral cytoplasmic membrane drug-proton transporter, an outer membrane channel and a periplasmic fusion protein linking the two other proteins. This assembly allows expelling the substrate from the inner membrane directly to the extracellular medium [Fig. 1, reproduced from [4]].
Ten efflux systems have been characterized in P. aeruginosa, among which MexAB-OprM and MeXY-OprM are constitutively expressed at a basal level in wild-type strains (expression of MeXY-OprM being however much lower than that of MexAB-OprM). Both systems are inducible when exposed to antibiotic substrates. The other systems (MexCD-OprJ, MexEF-OprN, MexJK, MexGHI-OpmD, MexVW, MexPQ-OpmE, MexMN, and TriABC are not expressed in wild type strains but may contribute to antibiotic or biocide resistance when expressed in resistant strains [5].
Antipseudomonal antibiotics released by P. aeruginosa multidrug efflux systems
RND efflux systems release multiple antimicrobials components including first-line antipseudomonal antibiotics such as β-lactams and β-lactamase inhibitors, fluoroquinolones, aminoglycosides [Table 1]. More specifically MexAB-OprM transports β-lactams fluoroquinolones, macrolides, tetracyclines, trimethoprim, sulfamides and chloramphenicol; MexXY-OprM, aminoglycosides, fluoroquinolones, macrolides, and tetracyclines; MexCD-oprJ, some β-lactams, fluoroquinolones, macrolides, tetracyclines, trimethoprim and chloramphenicol, and MexEF-OprN, fluoroquinolones, trimethoprim and chloramphenicol. The latter is also involved in resistance to meropenem and doripenem, but this may rather result from the fact that the OrpD porin is downregulated in strains expressing this efflux system.
Colistin, the last resort drug for MDR P. aeruginosa, is not substrate for these efflux pumps. Thus, efflux is responsible for multidrug resistance, a single pump being able to transport several classes of drugs while at the same time some redundancy exists among transporters, fluoroquinolones for example being universal substrates for the main efflux systems. Moreover, the subsequent reduction in antibiotic concentration inside the bacteria may help selecting high level resistance mechanisms, in particular target mutations [6].
Over-expression of efflux pumps: impact on antimicrobial susceptibility
A study published in 2010 examined the impact of antibiotic treatment on the susceptibility of P. aeruginosa, by collecting successive isolates from ICU patients at the time of diagnosis of infection and during treatment [7]. Globally, mean minimum inhibitory concentration (MIC) values increased after exposure to antibiotics, with statistically significant effects being observed for amikacin, ciprofloxacin, cefepime, meropenem and piperacillin/tazobactam, bringing mean MICs to values higher than the EUCAST susceptibility breakpoints. Three quarters of the isolates showed a moderate elevation of the MIC (≤16X initial MIC), suggesting the involvement of low to moderate levels resistance mechanisms as those affecting membrane permeability [Fig 2, reproduced from [7]].
The analysis of the expression of efflux pumps in this collection revealed that a high proportion of the strains (34 %) did overexpress MexAB-OprM and MexXY-OprM in the initial isolate, but that this proportion further increased during the antibiotic treatment, with about two third of the strain overexpressing at least one of these efflux systems [Fig.3, adapted from [8]].
Diagnosis of efflux in clinical laboratory
Because efflux in P. aeruginosa almost always co-operates with other mechanisms of resistance, differential diagnosis by phenotypic antimicrobial analysis is complex, high levels resistance mechanisms masking the effect of the expression of efflux systems on MICs. Moreover, efflux pumps can be overexpressed during treatment, which may explain therapeutic failures with antibiotics that are considered active based on the original determination of the susceptibility profile.
Resistance by efflux can be detected using Efflux Pumps Inhibitors (EPI), which revert MICs to those strains that do not express efflux systems. Among them MC-207,110 (phenylalanine arginyl beta-naphthylamide) is a broad spectrum inhibitor that has been widely used in vitro to investigate the impact of efflux on susceptibility to antibiotics of P. aeruginosa. Inhibitors specific of a given transporter are also under investigation. Yet, in MDR strains with additional resistance mechanisms, EPI do not allow restoring antibiotic activity, which may lead to false-negative results [9].
In this context, molecular methods appear as the only way to evidence the expression of efflux pumps in clinical isolates. Immunoblotting methods were developed first but were rapidly replaced by Reverse Transcriptase quantitative PCR (RT-qPCR) due to its higher specificity and rapidity. RT-qPCRs were thus developed to detect and quantify the expression of the genes coding for the different proteins of a given RND pump. This method remains applicable whatever the other resistance mechanisms present in the clinical strain and can thus be applied in clinical laboratories. Typically, a 2-fold increase in the expression of mexA and mexB genes causes a decrease in antibiotic susceptibility, while overexpression of mexX needs to be higher (≥ 5-fold) to increase MIC values. This low level of overexpression implies that all the steps for RT-qPCR should be carefully standardized [10]. The commercial mex Q-TesT kit includes two housekeeping genes to standardize the RT-qPCR and facilitates the interpretation of mexA and mexX genes expression of clinical Pseudomonas aeruginosa strains in comparison to wild type strain PAO1.
Conclusion
Resistance by efflux has now well been characterized in specialized laboratories but is still rarely or not at all diagnosed in routine laboratories. The complexity of resistance in P. aeruginosa with MDR phenotypes and the lack of diagnostic tools are probably the main reasons why this mechanism is neglected. Because this resistance mechanism can also contribute to therapeutic failures, accurate diagnosis is of prime importance for selecting adequate therapy.
References
1. Aliaga L, Mediavilla JD, et al. A clinical index predicting mortality with Pseudomonas aeruginosa bacteraemia. J Med Microbiol 2002; 51(7): 615-619.
2. Alp E, Guven M, et al. Incidence, risk factors and mortality of nosocomial pneumonia in intensive care units: a prospective study. Ann Clin Microbiol Antimicrob 2004; 3: 17.
3. Hirsch EB, Tam VH. Impact of multidrug-resistant Pseudomonas aeruginosa infection on patient outcomes. Expert Rev Pharmacoecon Outcomes Res 2010; 10(4): 441-451.
4. Mesaros N, Van Bambeke F, et al. L’efflux actif des antibiotiques et la résistance bactérienne: état de la question et implications. La lettre de l’infectiologue 2005; (4): 117-126.
5. Lister PD, Wolter DJ, et al. Antibacterial-resistant Pseudomonas aeruginosa: clinical impact and complex regulation of chromosomally encoded resistance mechanisms. Clin Microbiol Rev 2009; 22(4): 582-610.
6. Zhanel GG, Hoban DJ, et al. Role of efflux mechanisms on fluoroquinolone resistance in Streptococcus pneumoniae and Pseudomonas aeruginosa. Int J Antimicrob Agents 2004; 24(6): 529-535.
7. Riou M, Carbonnelle S, et al. In vivo development of antimicrobial resistance in Pseudomonas aeruginosa strains isolated from the lower respiratory tract of Intensive Care Unit patients with nosocomial pneumonia and receiving antipseudomonal therapy. Int J Antimicrob Agents 2010; 36(6): 513-522.
8. Riou M, Avrain L, et al. Influence of antibiotic treatments on gene expression of RND efflux pumps in successive isolates of Pseudomonas aeruginosa collected from patients with nosocomial pneumonia hospitalized in Intensive Care Units from Belgian Teaching Hospitals. ECCMID, 10-13 April 2010, Vienna, Austria. P780.
9. Van Bambeke F, Pages JM, et al. Inhibitors of bacterial efflux pumps as adjuvants in antibiotic treatments and diagnostic tools for detection of resistance by efflux. Recent Pat Antiinfect Drug Discov 2006; 1(2): 157-175.
10. Avrain L, Hocquet D, et al. Pre-Real-Time PCR steps standardization for appropriate interpretation of mexA and mexX gene expression by mex Q-Test in P. aeruginosa. ECCMID, 10-13 April 2010, Vienna, Austria. P590.
The authors
Laetitia Avrain PhD1*, Pascal Mertens PhD1 and Françoise Van Bambeke, Professor, Maître de Recherche FNRS, PhD2
1 Coris BioConcept, Gembloux, Belgium
2 Molecular and cellular pharmacology,
Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium
*Corresponding author
E-mail: laetitia.avrain@corisbio.com