Prostate Cancer and Prostatic Disease (2014) 17, 206–211 & 2014 Macmillan Publishers Limited All rights reserved 1365-7852/14 www.nature.com/pcan
ORIGINAL ARTICLE
Prostate cancer diagnosis through electronic nose in the urine headspace setting: a pilot study AD Asimakopoulos1, D Del Fabbro2, R Miano2, M Santonico3, R Capuano4, G Pennazza3, A D’Amico4 and E Finazzi-Agro`2 BACKGROUND: To evaluate the efficacy of prostate cancer (PCa) detection by the electronic nose (EN) on human urine samples. METHODS: Urine samples were obtained from candidates of prostate biopsy (PB). Exclusion criteria were a history of urothelial carcinoma or other malignant disease, urine infection, fasting for o12 h before PB or ingestion of alcohol or foods that might alter the urine smell in the last 24 h. The initial part of the voided urine and the midstream were collected separately in two sterile containers. Both samples were analyzed by the EN immediately after the collection. All patients underwent a standard transperineal, transrectal-ultrasound-guided PB. The pathological results were compared with the outcomes of the EN. Sensitivity and specificity of EN were assessed. RESULTS: Forty-one men were included in the study. Fourteen out of the 41 patients were positive for PCa. Midstream urine did not correlate significantly neither with a positive nor with a negative PB. Instead, significantly different results on the initial part of the urine stream between positive and negative PBs were obtained. The EN correctly recognized 10 out of the 14 cases (that is, sensitivity 71.4% (confidence interval (CI) 42–92%)) of PCa while four were false negatives. Moreover, the device recognized as negative 25 out of the 27 (that is, specificity 92.6% (CI 76–99%)) samples of negative PBs, with only two false positives. CONCLUSIONS: We believe this is the first demonstration of an olfactory imprinting of the initial part of the urine stream in patients with PCa that was revealed by an EN, with high specificity. Prostate Cancer and Prostatic Disease (2014) 17, 206–211; doi:10.1038/pcan.2014.11; published online 1 April 2014
INTRODUCTION Prostate cancer (PCa) is the most frequent noncutaneous malignancy in men.1 Although PSA blood testing remains the most widely used tool for PCa detection,2 it has been historically characterized by lack of specificity3 while none of the novel urine or blood biomarkers—proposed in the past decade—is currently widely used.4 Recently, Matsumura et al.,5 for the first time, established the feasibility of using urinary volatiles to detect lung cancer. In the field of PCa, Cornu et al.6 performed a double-blind study using an adequately trained dog in order to check its ability to detect PCa by sniffing urine. The results were surprising, with the dog correctly designating the cancer samples in 30 of the 33 cases, conferring a 91% of both specificity and sensitivity to the test. Although issues about the reproducibility (significantly affected by the canine olfactory receptor polymorphisms that influence odor detection performance of sniffer dogs7), costs and duration of the method were raised, we believe this study provided the first demonstration that PCa gives an odor signature to urine. Using the electronic nose (EN), Roine et al.8 recently proved that both malignant and nonmalignant prostate cell lines have distinct smell prints. Based on these findings, we herein present, to the best of our knowledge, the first study on the outcomes of the EN in the identification of PCa in urine samples of prostate biopsy (PB) candidates. Our aim was to evaluate the performance of this device as a conventional, non-invasive, quick and easily available instrument that, added to PSA, may improve PCa diagnosis.
MATERIALS AND METHODS Urine samples were obtained from Caucasian patients recruited in our center following written consent for analysis of their urine for research programs. All men included were consecutive candidates of PB. Our local ethics committee was informed of the study; a formal approval was considered unnecessary, because our observational preliminary study did not change the normal clinical practice: the urine samples used for the EN evaluation were immediately dismissed and they were not used for any other clinical or scientific purpose, while EN findings did not change the clinical behavior of the involved medical staff. A complete blood count, a basic metabolic panel (blood urea nitrogen, creatinine, glucose, electrolytes) and a urinanalysis, were required to all candidates to PB as standard clinical practice. Exclusion criteria for our study were a history of urothelial carcinoma or other malignant disease, any urine infection, fasting for o12 h before PB or ingestion of foods that might alter the urine smell (that is, asparagus, cauliflower, garlic, meat) in the past 24 h. Each patient was given two sterile containers for urine collection, one for the initial part of the voided urine and the other for the midstream. The urine sample collection was performed just before the PB, independently of the fact that it was or not the first voided urine of that morning. Volatile organic compound (VOC) analysis was performed before the PB and without knowing the PB results by medical and engineering staff not involved in the PB procedure. Furthermore, the staff performing the PB was not aware of the VOC analysis results. An adequately designed obturator was created in order to permit the extraction of the urine headspace necessary for the EN analysis (Figure 1). Both samples were then analyzed by last version of the EN as developed at the faculty of Engineering of the University of Rome ‘Tor Vergata’ (Figure 2).9 All measurements were performed within two hours from the collection of the urine samples, in order to maintain unaffected the
1 UOC of Urology, Department of Surgery, University of Rome Tor Vergata–Policlinico Casilino, Rome, Italy; 2UOC of Urology, Department of Surgery, University of Tor Vergata, Policlinico Tor Vergata, Rome, Italy; 3Center for Integrated Research—CIR, Unit of Electronics for Sensor Systems, Universita` Campus Bio-Medico, Rome, Italy and 4Department of Electronic Engineering, University of Tor Vergata, Rome, Italy. Correspondence: Dr AD Asimakopoulos, UOC of Urology, Department of Surgery, University of Rome Tor Vergata– Policlinico Casilino, Viale Oxford 81, Rome, 00133, Italy. E-mail: [email protected] Received 7 November 2013; revised 15 January 2014; accepted 9 February 2014; published online 1 April 2014
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Figure 1. The adequately designed obturator created in order to permit the extraction of the urine headspace, necessary for the electronic nose analysis.
cases or controls after pathological examination of the specimens. The peripheral zone of both lobes from the lateral to the paramedian area and from the base to the apex was sampled. For prostate volumes450 ml and on the basis of the clinical/ultrasound characteristics, additional cores were eventually obtained. A dedicated uropathologist examined the slides. Finally, the pathological results were compared with the outcomes of the EN. The overall graphic representation of the measurements obtained from the eight sensors requires an eight-dimensional space. A multivariate analysis obtained with supervised PLS-DA (Partial Least Square– Discriminant Analysis) permitted the graphic representation of the sensors’ measurements on a bi- or three-dimensional model. The PLS-DA model was built by using the leave-one-out as crossvalidation criterion. All the statistical data relative to the model are reported in Table 1. All the statistical parameters of the sensor array data set are reported in Figure 3. The classification model here presented was built using the PB outcomes as reference diagnosis.
Statistical methods The kappa statistic (k) was used as a measure of chance-corrected agreement between PB and EN. k-values ofo0.20, 0.21–0.40, 0.41–0.60 and40.61 represent poor, fair, moderate and good agreement, respectively. Sensitivity, specificity, positive predictive value—negative predictive value and their confidence interval (CI) were calculated.
E.N. FILTERS
SOFTWARE
URINE SAMPLE
Figure 2.
Electronic nose (E.N.).
olfactory characteristics of these samples. All the principles of the reference standard (European Standard, EN 13725, Air quality–Determination of odour concentration by dynamic olfactometry) that is used to provide a scientifically objective method for assessing odours were respected. The device used is equipped by eight non-selective gas sensors. Each sensor is coated with different metallo-porphyrins. Cu-Tetra Phenyl Porphyrin (TPP), Co-TPP, Zn-TPP, Mn-TPP, Fe-TPP, Sn-TPP, Ru-TPP and Cr-TPP are the eight metals selected, showing a large affinity toward multiple different VOCs. Each sensor (transducer þ metallo-porphyrin) adsorbs on its surface the VOCs and changes its frequency of resonance because of its mass variation as regulated by the proportional law of Sauerbrey.10 The interaction between the VOCs and the sensors is regulated by weak bonds, such as Van der Waals, dipole–dipole and hydrogen. The change on the frequency of each sensor with respect to the baseline constitutes the response of that sensor. The final output consists of a fingerprint of the eight sensor responses registered for each sample. A matrix containing all the measurements performed is then extracted by means of a dedicated software. Two hundred seconds were required for the analysis of each sample (with a flow rate of 50 Standard cc3 per min) while 600 s of flow with dehumidified air were required between two consecutive samples in order to clean the sensors. All the components with which the urine samples and the urine headspace get in contact during the analysis (teflon for the obturator and tygon for the tubes bringing the headspace to the sensors) are chemically inert, odorless and they present a low potential of absorption towards the VOCs that remain consequently not affected. Subsequently, all patients underwent a transperineal, transrectalultrasound-guided PB according to a standard procedure (12 cores) under local anaesthesia with an 18-G biopsy needle. They were classified as & 2014 Macmillan Publishers Limited
RESULTS Fifty consecutive patients referring to our center for PB were evaluated for eventual analysis of their urine headspace through EN. By applying the exclusion criteria, nine candidates to PB were excluded from the evaluation with EN. Consequently, 41 men aged 54–77 years were included in the study. Nine of them were smokers, seven were diabetics and 34 assumed oral medications for various pathologies. One patient was liver-transplanted. There was no statistically significant difference in the distribution of smokers vs non-smokers, alcohol consumers vs nonalcohol consumers and between patients under oral medications or not between the control and cancer group (Table 2). PSA ranged between 1.95 and 14.02 ng ml 1. A mean of 12.84 cores was obtained from the PBs (range 12–17). At the final histology, 14 patients (34%) were positive for PCa; 12 of them had a Gleason score of 6 (3 þ 3) and two a score of 7 (3 þ 4). Midstream urine did not correlate significantly neither with a positive nor with a negative PB; the outcomes on these samples were consequently excluded from the final analysis. On the contrary, we obtained significantly different results on the initial part of the urine stream between positive and negative PBs. The agreement between PB and EN was 85%, that is, significantly higher than the expected by chance (Po0.001) with a k value of 0.66. In particular, the EN correctly recognized 10 out of the 14 cases (that is, sensitivity 71.4% (CI 42–92%)) of PCa while four were false negatives. Both patients with Gleason 7 disease were correctly recognized by the EN. More importantly, the device recognized as negative 25 out of the 27 (that is, specificity 92.6% (CI 76–99%)) samples of negative PBs, with only two false positives (Table 3). The two false-positive patients were followed-up with digital rectum exam and PSA evaluation at 6 months after the initial biopsy. A rebiopsy was required to one of them for rising PSA, resulting again negative for PCa. Globally, the positive predictive value of the device is 83.3% (CI 52–98%), while the negative predictive value is 86.2% (CI 68–96%). Our results are depicted in Figure 4. The mean number of positive bioptic cores among the correctly identified PCa patients was 3.2 (range: 1 positive core with 10% involvement of the core–7 positive cores with 10–80% involvement of the cores). Prostate Cancer and Prostatic Disease (2014), 206 – 211
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208 300
Table 1.
Statistical parameters of the PLS-DA model calculated on the EN data, including the PRESS (cumulative prediction error sum of squares)
frequency shift [Hz]
250
200
150
100
50
0 1
2
3
4 5 sensors
6
7
8
Figure 3. Boxplot resuming all the statistical parameters of the sensor array data set. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually.
On this basis, it was possible to calculate the correct number of latent variables (LVs) to be selected in order to avoid overfitting (least RMSECV: root mean square error in cross validation). EN, electronic nose; PLS-DA, partial least square-discriminant analysis.
The mean number of positive bioptic cores among the four misclassified patients was 1.75 (range 1 positive core with 5% involvement–3 positive cores with 10–40% involved). No statistically significant difference between the mean number of positive cores between correctly identified PCa and misclassified patients was observed. Figure 5 represents the receiver operating characteristic curve for the discrimination between positive and negative subjects. Prostate Cancer and Prostatic Disease (2014), 206 – 211
DISCUSSION The early diagnosis of cancer constitutes a basic and diachronic objective of oncology. Animal models are increasingly used in this context. Canines are mainly adopted due to their superior olfactive apparatus, characterized by a detection threshold of parts per trillion. The scientific basis of this ability of dogs to detect the odor signature of cancer is linked to the VOCs produced by malignant cells.11 In fact, during tumor growth, protein changes in malignant cells lead to peroxidation of the cell membrane components and produce VOCs that can be detected in the headspace of the cells.12,13 The utilization of dogs for cancer detection merged after the first case report in 1989 about a melanoma detected by a dog on his owner’s leg.14 There followed another study on melanoma detection by dogs directly on tissue that was correct in 75–86% of the cases.15 Other studies documented an impressive performance of the canine olfatus in detecting lung cancer by sniffing the exhaled breath (sensitivity 71%, specificity 93%, independent of the presence of chronic obstructive pulmonary disorder or tobacco smoke),16 ovarian cancer in both blood and tissue samples (reaching a sensitivity of 100% and specificity of 98%),17 breast cancer in breath samples (sensitivity and specificity of dog detection of 88% and 98%, respectively),18 colorectal cancer (sensitivity of 91% and 97% in breath and fecal samples, respectively, and a specificity of 99% for both sample types)19 and bladder cancer by sniffing urine samples, with a diagnostic success rate of 41%.20 By sniffing urine, dogs can accurately detect lung cancer5 as well as bladder or breast cancer with better than chance probability,21 although no positive result was obtained for PCa.22 In the specific field of PCa, Cornu et al.6 showed for the first time that a trained dog can distinguish a PCa urine sample among controls with powerful results: the dog correctly designating the cancer samples in 30 of the 33 cases, conferring a 91% of both specificity and sensitivity to the test. The main success of this study was the demonstration that PCa gives an odor signature to the urine. However, these results were obtained with only one dog, questioning the reproducibility of this method. Furthermore, the type of dog used in the study could have influenced the results as canine olfactory receptor polymorphisms have been shown to & 2014 Macmillan Publishers Limited
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209 Table 2.
Effect of the confounding factors on the outcomes of the EN Negative
Positive
Marginal row totals
Smoking Non-smoking Marginal column totals
6 (5.93) [0] 21 (21.07) [0] 27
3 (3.07) [0] 11(10.93) [0] 14
9 32 41 (grand total)a
Alcohol Non-alcohol Marginal column totals
6 (7.24) [0.21] 21 (19.76) [0.08] 27
5 (3.76) [0.41] 9 (10.24) [0.15] 14
11 30 41 (grand total)b
Pharma Non-pharma Marginal column totals
22 (22.39) [0.01] 5 (4.61) [0.03] 27
12 (11.61) [0.01] 2 (10.24) [0.06] 14
34 7 41 (grand total)c
Abbreviation: EN, electronic nose. a The w2 statistic is 0.003. The P-value is 0.953574. This result is not significant at Po0.05. b The w2 statistic is 0.8549. The P-value is 0.355168. This result is not significant at Po0.05. c The w2 statistic is 0.1167. The P-value is 0.732676. This result is not significant at Po0.05.
Table 3.
Suggested areas to focus the future research on the EN
Abbreviations: EN, electronic nose; PCa, prostate cancer; VOC, volatile organic compound.
influence odor detection performance by sniffer dogs.7 The study was costly and long in duration, making it difficult to conceive of an extended use for this test in clinical practice. Finally, concerns regarding the lack of communication with the dogs, the difficulties of an adequate training, several hygienic and even ethical issues regarding dogs subjected to forced inhalation should be further considered. Although canine cancer detection is not feasible for widespread clinical use, these studies have inspired a race between scientists to develop suitably sensitive analytical instrumentation for quick and easy clinical use that show similar accuracy to the mammalian sense of smell.23 By the term EN it is intended an instrument able to generate digital maps of complex odours or chemical images. The fundamental elements of the EN that we used are the piezoelectric chemical sensors which operating as miniaturized transducers do respond reversely to the chemical volatile compounds generating electrical signals in function of the & 2014 Macmillan Publishers Limited
gas concentration in real time. The operating principle of the piezoelectric chemical sensors (also defined mass sensors) located in the EN narici, is based on the variation of frequency of resonating quartz crystals (Thickness Shear Mode Resonators) caused by their mass variation, that its turn is determined by the adsorption or desorption processes of gas molecules on the chemical interactive materials (metal-tetrafenilporfirine) of the sensors. The odour classification is obtained by a kind of multivariate statistical analysis. The interaction between VOC with an array of sensors generates a characteristic fingerprint, which can be recognized by comparing it with previously recorded patterns in the recognition system. The common features of all ENs are the relatively fast response and the satisfactory stability. These devices are used in the food and agricultural sectors, in the ambient monitoring, in space applications and for security issues. In the medical field, they are used as diagnostic instruments for skin disease and internal Prostate Cancer and Prostatic Disease (2014), 206 – 211
Prostate cancer and electronic nose AD Asimakopoulos et al
210 Scores Plot
4
Loads Plot
0.6
3
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2
LV 2 (25.01%)
LV 1 (38.55%)
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0 −1 −2
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0.1
Prostate cancer −3
0 −4 −0.1 1
−5 −6
−4
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4
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Figure 4. Scores plot of the latent variables (LV) 1 and 2 obtained by the PLS-DA (Partial Least Square–Discriminant Analysis) model. It is evident the discrimination between control subjects (label 0) and tumor cases (label 1).
4 5 Variable Number
6
7
8
6
7
8
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0.3 0.2 0.1
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0.00 0.00
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Figure 6. Loading plots for the latent variables (LV)1 of the PLS-DA (Partial Least Square–Discriminant Analysis) model calculated on the electronic nose data. Non-negligible load values can be observed, meaning that all the sensors give an important contribution to the patient’s discrimination.
Area under ROC curve = 0.8915
Figure 5. Receiver operating characteristic (ROC) curve with line of identity of breathprint latent variable 1 predictive for prostate cancer diagnosis. (area under the ROC curve 0.8915).
pathology detection, such as diabetes, cancers, urinary infections, tuberculosis, Helicobacter pylori and so on.21,24 Focusing on the field of oncology, dynamic olfactometry, obtained with EN, is currently studied as an alternative to the animal model for the early detection of several human tumors. Successful detection of lung,25–30 gastric,31 ovarian,32 colorectal,33 breast18,34 and skin cancer35 by EN are well described in the published literature, with results similar, even if somewhat lower, to those of the dogs. In the field of urological oncology, the EN has already shown promising results concerning the detection of urinary tract tumors in urine samples. EN was able to discriminate urine samples of healthy patients from those of patients with bladder cancer, with a diagnostic accuracy of 100%,36 overcoming the one described for dogs.20 The diagnostic accuracy of EN in PCa was not verified till recently, when Roine et al.8 analyzed through EN the smell Prostate Cancer and Prostatic Disease (2014), 206 – 211
prints of collected prostatic nonmalignant (EP-156 T and controls) and malignant (LNCaP) cell lines. The device differentiated the nonmalignant and malignant cell lines from each other with very low misclassification rates (2.9–3.6%), suggesting that malignant and nonmalignant cell lines have distinct smell prints.8 With our study, we demonstrated, we believe for the first time, in the clinical setting (and not in cell lines) a potential role of the EN in identifying, by smelling urine samples, healthy patients (with negative PB) while maintaining an acceptable sensitivity. The overall agreement between biopsy and EN was 85% while the k value (0.66) indicates a good agreement between the two methods. The first part of the urine stream (and not the midstream) allowed to distinguish between positive and negative PBs, probably because it contains elements of prostatic secretion. The influence of eventual confounding factors that might alter the urine smell influencing the EN accuracy has been also evaluated. Alcohol, oral medications and smoke do not seem to influence the accuracy of the device. Overall, the results obtainable with the EN are chemical images with a rather high probability of cancer recognition. The high specificity of the device, if confirmed, could allow to reduce the & 2014 Macmillan Publishers Limited
Prostate cancer and electronic nose AD Asimakopoulos et al
211 number of first PBs or rebiopsies. It would appear that if the test results negative, the PB will be almost always (93%) negative; thus, both extra costs to the health-care system and potential complications to the patients of further diagnostic procedures may be spared. Two issues regarding the sensors should be further commented: their statistical contribution and their consistency with respect to prostate disease-alleged biomarkers. It is worth mentioning that the used EN consists of an array of non-selective gas sensors, thus each latent variable is a linear combination of all the sensors: they search for characteristic smell prints of particular conditions in the urine headspace without distinguishing the constituent VOCs. Over-fitting condition is avoided by the selection of the correct number of LVs (see Table 1). Moreover, as can be observed in the loading plots in Figure 6, none of the sensors in the array shows negligible load values with respect to the other ones. Concerning sensors’ pertinence with respect to PCa diagnosis, the possible discrimination of cancer is based on fingerprint differentiation, thus the volatile biomarkers of PCa remain to be identified, in order to use selective adsorbing material in sensor arrays. Some limitations of our study should be mentioned. The number of cases is small to permit definitive conclusions on the diagnostic accuracy of the EN, although the preliminary data appears at least promising. Only two cases of Gleason score 7 disease were included in the study, and none with Gleason scoreX8; consequently, the ability of the device in the diagnosis of high-grade PCa cannot be evaluated. However, it seems promising that both cases of Gleason 7 were correctly classified by the device as positive. The minimum number of positive cores/ minimum percentage of core involvement that correlates to an accurate diagnosis were not addressed. However, in our preliminary analysis, there was not a significant difference between mean number of positive cores in correctly identified PCa vs misclassified patients. Finally, any correlation between the EN results and the outcomes of the radical prostatectomy specimen was not explored. Table 3 suggests crucial areas for further future research on EN. CONCLUSIONS To the best of our knowledge, this is the first demonstration of an olfactory imprinting of the initial part of the urine stream in patients with PCa that was accurately revealed by an EN. The high specificity of the device, if confirmed in larger studies, may overcome the known specificity gap of the PSA serum test, and it may permit a more accurate selection of the PB candidates. CONFLICT OF INTEREST The authors declare no conflict of interest.
REFERENCES 1 Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin 2009; 59: 225–249. 2 Schro¨der FH. PSA screening—a review of recent studies. Eur J Cancer 2009; 45(Suppl 1): 402–404. 3 Stamey TA, Caldwell M, McNeal JE, Nolley R, Hemenez M, Downs J. The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? J Urol 2004; 172: 1297–1301. 4 Tuma RS. New tests for prostate cancer may be nearing the clinic. J Natl Cancer Inst 2010; 102: 752–754. 5 Matsumura K, Opiekun M, Oka H, Vachani A, Albelda SM, Yamazaki K et al. Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer. PLoS One 2010; 5: e8819. 6 Cornu JN, Cancel-Tassin G, Ondet V, Girardet C, Cussenot O. Olfactory detection of prostate cancer by dogs sniffing urine: a step forward in early diagnosis. Eur Urol 2011; 59: 197–201.
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7 Lesniak A, Walczak M, Jezierski T, Sacharczuk M, Gawkowski M, Jaszczak K. Canine olfactory receptor gene polymorphism and its relation to odor detection performance by sniffer dogs. J Hered 2008; 99: 518–527. 8 Roine A, Tolvanen M, Sipila¨inen M, Kumpulainen P, Helenius MA, Lehtima¨ki T et al. Detection of smell print differences between nonmalignant and malignant prostate cells with an electronic nose. Future Oncol 2012; 8: 1157–1165. 9 Santonico M, Pennazza G, Capuano R, Falconi C, Vink TJ, Knobel HH et al. Electronic noses calibration procedure in the context of a multicentre medical study. Sens Actuators B Cheml 2012; 173: 555–561. 10 Sauerbrey G. Verwendung von Schwingquarzen zur Wa¨gung du¨nner Schichten und zur Mikrowa¨gung. Z Phys 1959; 155: 206. 11 Kneepkens CM, Lepage G, Roy CC. The potential of the hydrocarbon breath test as a measure of lipid peroxidation. Free Radic Biol Med 1994; 17: 127–160. 12 Bajaj A, Miranda OR, Kim IB, Phillips RL, Jerry DJ, Bunz UH et al. Detection and differentiation of normal, cancerous, and metastatic cells using nanoparticlepolymer sensor arrays. Proc Natl Acad Sci USA. 2009; 106: 10912–10916. 13 Filipiak W, Sponring A, Filipiak A, Ager C, Schubert J, Miekisch W et al. TD-GC-MS analysis of volatile metabolites of human lung cancer and normal cells in vitro. Cancer Epidemiol Biomarkers Prev 2010; 19: 182–195. 14 Williams H, Pembroke A. Sniffer dogs in the melanoma clinic? Lancet 1989; 1: 734. 15 Pickel D, Manucy GP, Walker DB, Hall SB, Walker JC. Evidence for canine olfactory detection of melanoma. Appl Anim Behav Sci 2004; 89: 107–116. 16 Ehmann R, Boedeker E, Friedrich U, Sagert J, Dippon J, Friedel G et al. Canine scent detection in the diagnosis of lung cancer: revisiting a puzzling phenomenon. Eur Respir J 2012; 39: 669–676. 17 Horvath G, Andersson H, Paulsson G. Characteristic odour in the blood reveals ovarian carcinoma. BMC Cancer 2010; 10: 643. 18 McCulloch M, Jezierski T, Broffman M, Hubbard A, Turner K, Janecki T. Diagnostic accuracy of canine scent detection in early- and late-stage lung and breast cancers. Integr Cancer Ther 2006; 5: 30–39. 19 Sonoda H, Kohnoe S, Yamazato T, Satoh Y, Morizono G, Shikata K et al. Colorectal cancer screening with odour material by canine scent detection. Gut 2011; 60: 814–819. 20 Willis CM, Church SM, Guest CM, Cook WA, McCarthy N, Bransbury AJ et al. Olfactory detection of human bladder cancer by dogs: proof of principle study. BMJ 2004; 329: 712. 21 Bijland LR, Bomers MK, Smulders YM. Smelling the diagnosis: a review on the use of scent in diagnosing disease. Neth J Med 2013; 71: 300–307. 22 Gordon RT, Schatz CB, Myers LJ, Kosty M, Gonczy C, Kroener J et al. The use of canines in the detection of human cancers. J Altern Complement Med 2008; 14: 61–67. 23 Leja MA, Liu H, Haick H. Breath testing: the future for digestive cancer detection. Expert Rev Gastroenterol Hepatol 2013; 7: 389–391. 24 http://www.cnr.it/documenti/Istituzionali/Consuntivo/Report/Report2003_file/pdf/ 237-238Focus.pdf. 25 Bajtarevic A, Ager C, Pienz M, Klieber M, Schwarz K, Ligor M et al. Noninvasive detection of lung cancer by analysis of exhaled breath. BMC Cancer 2009; 9: 348. 26 D’Amico A, Pennazza G, Santonico M, Martinelli E, Roscioni C, Galluccio G et al. An investigation on electronic nose diagnosis of lung cancer. Lung Cancer 2010; 68: 170–176. 27 Di Natale C, Macagnano A, Martinelli E, Paolesse R, D’Arcangelo G, Roscioni C et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 2003; 18: 1209–1218. 28 Machado RF, Laskowski D, Deffenderfer O, Burch T, Zheng S, Mazzone PJ et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 2005; 171: 1286–1291. 29 Barash O, Peled N, Hirsch FR, Haick H. Sniffing the unique "odor print" of nonsmall-cell lung cancer with gold nanoparticles. Small 2009; 5: 2618–2624. 30 Peled N, Hakim M, Bunn Jr PA, Miller YE, Kennedy TC, Mattei J et al. Non-invasive breath analysis of pulmonary nodules. J Thorac Oncol 2012; 7: 1528–1533. 31 Xu ZQ, Broza YY, Ionsecu R, Tisch U, Ding L, Liu H et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer 2013; 108: 941–950. 32 Horvath G, Chilo J, Lindblad T. Different volatile signals emitted by human ovarian carcinoma and healthy tissue. Future Oncol 2010; 6: 1043–1049. 33 Peng G, Hakim M, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br J Cancer 2010; 103: 542–551. 34 Phillips M, Cataneo RN, Saunders C, Hope P, Schmitt P, Wai J. Volatile biomarkers in the breath of women with breast cancer. J Breath Res 2010; 4: 026003. 35 D’Amico A, Bono R, Pennazza G, Santonico M, Mantini G, Bernabei M et al. Identification of melanoma with a gas sensor array. Skin Res Technol 2008; 14: 226–236. 36 Bernabei M, Pennazza G, Santonico M, Corsi C, Roscioni C, Paolesse R et al. A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sens Actuators B Chem 2008; 131: 1–4.
Prostate Cancer and Prostatic Disease (2014), 206 – 211
ORIGINAL ARTICLE
Prostate cancer diagnosis through electronic nose in the urine headspace setting: a pilot study AD Asimakopoulos1, D Del Fabbro2, R Miano2, M Santonico3, R Capuano4, G Pennazza3, A D’Amico4 and E Finazzi-Agro`2 BACKGROUND: To evaluate the efficacy of prostate cancer (PCa) detection by the electronic nose (EN) on human urine samples. METHODS: Urine samples were obtained from candidates of prostate biopsy (PB). Exclusion criteria were a history of urothelial carcinoma or other malignant disease, urine infection, fasting for o12 h before PB or ingestion of alcohol or foods that might alter the urine smell in the last 24 h. The initial part of the voided urine and the midstream were collected separately in two sterile containers. Both samples were analyzed by the EN immediately after the collection. All patients underwent a standard transperineal, transrectal-ultrasound-guided PB. The pathological results were compared with the outcomes of the EN. Sensitivity and specificity of EN were assessed. RESULTS: Forty-one men were included in the study. Fourteen out of the 41 patients were positive for PCa. Midstream urine did not correlate significantly neither with a positive nor with a negative PB. Instead, significantly different results on the initial part of the urine stream between positive and negative PBs were obtained. The EN correctly recognized 10 out of the 14 cases (that is, sensitivity 71.4% (confidence interval (CI) 42–92%)) of PCa while four were false negatives. Moreover, the device recognized as negative 25 out of the 27 (that is, specificity 92.6% (CI 76–99%)) samples of negative PBs, with only two false positives. CONCLUSIONS: We believe this is the first demonstration of an olfactory imprinting of the initial part of the urine stream in patients with PCa that was revealed by an EN, with high specificity. Prostate Cancer and Prostatic Disease (2014) 17, 206–211; doi:10.1038/pcan.2014.11; published online 1 April 2014
INTRODUCTION Prostate cancer (PCa) is the most frequent noncutaneous malignancy in men.1 Although PSA blood testing remains the most widely used tool for PCa detection,2 it has been historically characterized by lack of specificity3 while none of the novel urine or blood biomarkers—proposed in the past decade—is currently widely used.4 Recently, Matsumura et al.,5 for the first time, established the feasibility of using urinary volatiles to detect lung cancer. In the field of PCa, Cornu et al.6 performed a double-blind study using an adequately trained dog in order to check its ability to detect PCa by sniffing urine. The results were surprising, with the dog correctly designating the cancer samples in 30 of the 33 cases, conferring a 91% of both specificity and sensitivity to the test. Although issues about the reproducibility (significantly affected by the canine olfactory receptor polymorphisms that influence odor detection performance of sniffer dogs7), costs and duration of the method were raised, we believe this study provided the first demonstration that PCa gives an odor signature to urine. Using the electronic nose (EN), Roine et al.8 recently proved that both malignant and nonmalignant prostate cell lines have distinct smell prints. Based on these findings, we herein present, to the best of our knowledge, the first study on the outcomes of the EN in the identification of PCa in urine samples of prostate biopsy (PB) candidates. Our aim was to evaluate the performance of this device as a conventional, non-invasive, quick and easily available instrument that, added to PSA, may improve PCa diagnosis.
MATERIALS AND METHODS Urine samples were obtained from Caucasian patients recruited in our center following written consent for analysis of their urine for research programs. All men included were consecutive candidates of PB. Our local ethics committee was informed of the study; a formal approval was considered unnecessary, because our observational preliminary study did not change the normal clinical practice: the urine samples used for the EN evaluation were immediately dismissed and they were not used for any other clinical or scientific purpose, while EN findings did not change the clinical behavior of the involved medical staff. A complete blood count, a basic metabolic panel (blood urea nitrogen, creatinine, glucose, electrolytes) and a urinanalysis, were required to all candidates to PB as standard clinical practice. Exclusion criteria for our study were a history of urothelial carcinoma or other malignant disease, any urine infection, fasting for o12 h before PB or ingestion of foods that might alter the urine smell (that is, asparagus, cauliflower, garlic, meat) in the past 24 h. Each patient was given two sterile containers for urine collection, one for the initial part of the voided urine and the other for the midstream. The urine sample collection was performed just before the PB, independently of the fact that it was or not the first voided urine of that morning. Volatile organic compound (VOC) analysis was performed before the PB and without knowing the PB results by medical and engineering staff not involved in the PB procedure. Furthermore, the staff performing the PB was not aware of the VOC analysis results. An adequately designed obturator was created in order to permit the extraction of the urine headspace necessary for the EN analysis (Figure 1). Both samples were then analyzed by last version of the EN as developed at the faculty of Engineering of the University of Rome ‘Tor Vergata’ (Figure 2).9 All measurements were performed within two hours from the collection of the urine samples, in order to maintain unaffected the
1 UOC of Urology, Department of Surgery, University of Rome Tor Vergata–Policlinico Casilino, Rome, Italy; 2UOC of Urology, Department of Surgery, University of Tor Vergata, Policlinico Tor Vergata, Rome, Italy; 3Center for Integrated Research—CIR, Unit of Electronics for Sensor Systems, Universita` Campus Bio-Medico, Rome, Italy and 4Department of Electronic Engineering, University of Tor Vergata, Rome, Italy. Correspondence: Dr AD Asimakopoulos, UOC of Urology, Department of Surgery, University of Rome Tor Vergata– Policlinico Casilino, Viale Oxford 81, Rome, 00133, Italy. E-mail: [email protected] Received 7 November 2013; revised 15 January 2014; accepted 9 February 2014; published online 1 April 2014
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Figure 1. The adequately designed obturator created in order to permit the extraction of the urine headspace, necessary for the electronic nose analysis.
cases or controls after pathological examination of the specimens. The peripheral zone of both lobes from the lateral to the paramedian area and from the base to the apex was sampled. For prostate volumes450 ml and on the basis of the clinical/ultrasound characteristics, additional cores were eventually obtained. A dedicated uropathologist examined the slides. Finally, the pathological results were compared with the outcomes of the EN. The overall graphic representation of the measurements obtained from the eight sensors requires an eight-dimensional space. A multivariate analysis obtained with supervised PLS-DA (Partial Least Square– Discriminant Analysis) permitted the graphic representation of the sensors’ measurements on a bi- or three-dimensional model. The PLS-DA model was built by using the leave-one-out as crossvalidation criterion. All the statistical data relative to the model are reported in Table 1. All the statistical parameters of the sensor array data set are reported in Figure 3. The classification model here presented was built using the PB outcomes as reference diagnosis.
Statistical methods The kappa statistic (k) was used as a measure of chance-corrected agreement between PB and EN. k-values ofo0.20, 0.21–0.40, 0.41–0.60 and40.61 represent poor, fair, moderate and good agreement, respectively. Sensitivity, specificity, positive predictive value—negative predictive value and their confidence interval (CI) were calculated.
E.N. FILTERS
SOFTWARE
URINE SAMPLE
Figure 2.
Electronic nose (E.N.).
olfactory characteristics of these samples. All the principles of the reference standard (European Standard, EN 13725, Air quality–Determination of odour concentration by dynamic olfactometry) that is used to provide a scientifically objective method for assessing odours were respected. The device used is equipped by eight non-selective gas sensors. Each sensor is coated with different metallo-porphyrins. Cu-Tetra Phenyl Porphyrin (TPP), Co-TPP, Zn-TPP, Mn-TPP, Fe-TPP, Sn-TPP, Ru-TPP and Cr-TPP are the eight metals selected, showing a large affinity toward multiple different VOCs. Each sensor (transducer þ metallo-porphyrin) adsorbs on its surface the VOCs and changes its frequency of resonance because of its mass variation as regulated by the proportional law of Sauerbrey.10 The interaction between the VOCs and the sensors is regulated by weak bonds, such as Van der Waals, dipole–dipole and hydrogen. The change on the frequency of each sensor with respect to the baseline constitutes the response of that sensor. The final output consists of a fingerprint of the eight sensor responses registered for each sample. A matrix containing all the measurements performed is then extracted by means of a dedicated software. Two hundred seconds were required for the analysis of each sample (with a flow rate of 50 Standard cc3 per min) while 600 s of flow with dehumidified air were required between two consecutive samples in order to clean the sensors. All the components with which the urine samples and the urine headspace get in contact during the analysis (teflon for the obturator and tygon for the tubes bringing the headspace to the sensors) are chemically inert, odorless and they present a low potential of absorption towards the VOCs that remain consequently not affected. Subsequently, all patients underwent a transperineal, transrectalultrasound-guided PB according to a standard procedure (12 cores) under local anaesthesia with an 18-G biopsy needle. They were classified as & 2014 Macmillan Publishers Limited
RESULTS Fifty consecutive patients referring to our center for PB were evaluated for eventual analysis of their urine headspace through EN. By applying the exclusion criteria, nine candidates to PB were excluded from the evaluation with EN. Consequently, 41 men aged 54–77 years were included in the study. Nine of them were smokers, seven were diabetics and 34 assumed oral medications for various pathologies. One patient was liver-transplanted. There was no statistically significant difference in the distribution of smokers vs non-smokers, alcohol consumers vs nonalcohol consumers and between patients under oral medications or not between the control and cancer group (Table 2). PSA ranged between 1.95 and 14.02 ng ml 1. A mean of 12.84 cores was obtained from the PBs (range 12–17). At the final histology, 14 patients (34%) were positive for PCa; 12 of them had a Gleason score of 6 (3 þ 3) and two a score of 7 (3 þ 4). Midstream urine did not correlate significantly neither with a positive nor with a negative PB; the outcomes on these samples were consequently excluded from the final analysis. On the contrary, we obtained significantly different results on the initial part of the urine stream between positive and negative PBs. The agreement between PB and EN was 85%, that is, significantly higher than the expected by chance (Po0.001) with a k value of 0.66. In particular, the EN correctly recognized 10 out of the 14 cases (that is, sensitivity 71.4% (CI 42–92%)) of PCa while four were false negatives. Both patients with Gleason 7 disease were correctly recognized by the EN. More importantly, the device recognized as negative 25 out of the 27 (that is, specificity 92.6% (CI 76–99%)) samples of negative PBs, with only two false positives (Table 3). The two false-positive patients were followed-up with digital rectum exam and PSA evaluation at 6 months after the initial biopsy. A rebiopsy was required to one of them for rising PSA, resulting again negative for PCa. Globally, the positive predictive value of the device is 83.3% (CI 52–98%), while the negative predictive value is 86.2% (CI 68–96%). Our results are depicted in Figure 4. The mean number of positive bioptic cores among the correctly identified PCa patients was 3.2 (range: 1 positive core with 10% involvement of the core–7 positive cores with 10–80% involvement of the cores). Prostate Cancer and Prostatic Disease (2014), 206 – 211
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208 300
Table 1.
Statistical parameters of the PLS-DA model calculated on the EN data, including the PRESS (cumulative prediction error sum of squares)
frequency shift [Hz]
250
200
150
100
50
0 1
2
3
4 5 sensors
6
7
8
Figure 3. Boxplot resuming all the statistical parameters of the sensor array data set. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually.
On this basis, it was possible to calculate the correct number of latent variables (LVs) to be selected in order to avoid overfitting (least RMSECV: root mean square error in cross validation). EN, electronic nose; PLS-DA, partial least square-discriminant analysis.
The mean number of positive bioptic cores among the four misclassified patients was 1.75 (range 1 positive core with 5% involvement–3 positive cores with 10–40% involved). No statistically significant difference between the mean number of positive cores between correctly identified PCa and misclassified patients was observed. Figure 5 represents the receiver operating characteristic curve for the discrimination between positive and negative subjects. Prostate Cancer and Prostatic Disease (2014), 206 – 211
DISCUSSION The early diagnosis of cancer constitutes a basic and diachronic objective of oncology. Animal models are increasingly used in this context. Canines are mainly adopted due to their superior olfactive apparatus, characterized by a detection threshold of parts per trillion. The scientific basis of this ability of dogs to detect the odor signature of cancer is linked to the VOCs produced by malignant cells.11 In fact, during tumor growth, protein changes in malignant cells lead to peroxidation of the cell membrane components and produce VOCs that can be detected in the headspace of the cells.12,13 The utilization of dogs for cancer detection merged after the first case report in 1989 about a melanoma detected by a dog on his owner’s leg.14 There followed another study on melanoma detection by dogs directly on tissue that was correct in 75–86% of the cases.15 Other studies documented an impressive performance of the canine olfatus in detecting lung cancer by sniffing the exhaled breath (sensitivity 71%, specificity 93%, independent of the presence of chronic obstructive pulmonary disorder or tobacco smoke),16 ovarian cancer in both blood and tissue samples (reaching a sensitivity of 100% and specificity of 98%),17 breast cancer in breath samples (sensitivity and specificity of dog detection of 88% and 98%, respectively),18 colorectal cancer (sensitivity of 91% and 97% in breath and fecal samples, respectively, and a specificity of 99% for both sample types)19 and bladder cancer by sniffing urine samples, with a diagnostic success rate of 41%.20 By sniffing urine, dogs can accurately detect lung cancer5 as well as bladder or breast cancer with better than chance probability,21 although no positive result was obtained for PCa.22 In the specific field of PCa, Cornu et al.6 showed for the first time that a trained dog can distinguish a PCa urine sample among controls with powerful results: the dog correctly designating the cancer samples in 30 of the 33 cases, conferring a 91% of both specificity and sensitivity to the test. The main success of this study was the demonstration that PCa gives an odor signature to the urine. However, these results were obtained with only one dog, questioning the reproducibility of this method. Furthermore, the type of dog used in the study could have influenced the results as canine olfactory receptor polymorphisms have been shown to & 2014 Macmillan Publishers Limited
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209 Table 2.
Effect of the confounding factors on the outcomes of the EN Negative
Positive
Marginal row totals
Smoking Non-smoking Marginal column totals
6 (5.93) [0] 21 (21.07) [0] 27
3 (3.07) [0] 11(10.93) [0] 14
9 32 41 (grand total)a
Alcohol Non-alcohol Marginal column totals
6 (7.24) [0.21] 21 (19.76) [0.08] 27
5 (3.76) [0.41] 9 (10.24) [0.15] 14
11 30 41 (grand total)b
Pharma Non-pharma Marginal column totals
22 (22.39) [0.01] 5 (4.61) [0.03] 27
12 (11.61) [0.01] 2 (10.24) [0.06] 14
34 7 41 (grand total)c
Abbreviation: EN, electronic nose. a The w2 statistic is 0.003. The P-value is 0.953574. This result is not significant at Po0.05. b The w2 statistic is 0.8549. The P-value is 0.355168. This result is not significant at Po0.05. c The w2 statistic is 0.1167. The P-value is 0.732676. This result is not significant at Po0.05.
Table 3.
Suggested areas to focus the future research on the EN
Abbreviations: EN, electronic nose; PCa, prostate cancer; VOC, volatile organic compound.
influence odor detection performance by sniffer dogs.7 The study was costly and long in duration, making it difficult to conceive of an extended use for this test in clinical practice. Finally, concerns regarding the lack of communication with the dogs, the difficulties of an adequate training, several hygienic and even ethical issues regarding dogs subjected to forced inhalation should be further considered. Although canine cancer detection is not feasible for widespread clinical use, these studies have inspired a race between scientists to develop suitably sensitive analytical instrumentation for quick and easy clinical use that show similar accuracy to the mammalian sense of smell.23 By the term EN it is intended an instrument able to generate digital maps of complex odours or chemical images. The fundamental elements of the EN that we used are the piezoelectric chemical sensors which operating as miniaturized transducers do respond reversely to the chemical volatile compounds generating electrical signals in function of the & 2014 Macmillan Publishers Limited
gas concentration in real time. The operating principle of the piezoelectric chemical sensors (also defined mass sensors) located in the EN narici, is based on the variation of frequency of resonating quartz crystals (Thickness Shear Mode Resonators) caused by their mass variation, that its turn is determined by the adsorption or desorption processes of gas molecules on the chemical interactive materials (metal-tetrafenilporfirine) of the sensors. The odour classification is obtained by a kind of multivariate statistical analysis. The interaction between VOC with an array of sensors generates a characteristic fingerprint, which can be recognized by comparing it with previously recorded patterns in the recognition system. The common features of all ENs are the relatively fast response and the satisfactory stability. These devices are used in the food and agricultural sectors, in the ambient monitoring, in space applications and for security issues. In the medical field, they are used as diagnostic instruments for skin disease and internal Prostate Cancer and Prostatic Disease (2014), 206 – 211
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210 Scores Plot
4
Loads Plot
0.6
3
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LV 2 (25.01%)
LV 1 (38.55%)
0.4 1
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0 −1 −2
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0.1
Prostate cancer −3
0 −4 −0.1 1
−5 −6
−4
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4
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3
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Figure 4. Scores plot of the latent variables (LV) 1 and 2 obtained by the PLS-DA (Partial Least Square–Discriminant Analysis) model. It is evident the discrimination between control subjects (label 0) and tumor cases (label 1).
4 5 Variable Number
6
7
8
6
7
8
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0.3 0.2 0.1
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Figure 6. Loading plots for the latent variables (LV)1 of the PLS-DA (Partial Least Square–Discriminant Analysis) model calculated on the electronic nose data. Non-negligible load values can be observed, meaning that all the sensors give an important contribution to the patient’s discrimination.
Area under ROC curve = 0.8915
Figure 5. Receiver operating characteristic (ROC) curve with line of identity of breathprint latent variable 1 predictive for prostate cancer diagnosis. (area under the ROC curve 0.8915).
pathology detection, such as diabetes, cancers, urinary infections, tuberculosis, Helicobacter pylori and so on.21,24 Focusing on the field of oncology, dynamic olfactometry, obtained with EN, is currently studied as an alternative to the animal model for the early detection of several human tumors. Successful detection of lung,25–30 gastric,31 ovarian,32 colorectal,33 breast18,34 and skin cancer35 by EN are well described in the published literature, with results similar, even if somewhat lower, to those of the dogs. In the field of urological oncology, the EN has already shown promising results concerning the detection of urinary tract tumors in urine samples. EN was able to discriminate urine samples of healthy patients from those of patients with bladder cancer, with a diagnostic accuracy of 100%,36 overcoming the one described for dogs.20 The diagnostic accuracy of EN in PCa was not verified till recently, when Roine et al.8 analyzed through EN the smell Prostate Cancer and Prostatic Disease (2014), 206 – 211
prints of collected prostatic nonmalignant (EP-156 T and controls) and malignant (LNCaP) cell lines. The device differentiated the nonmalignant and malignant cell lines from each other with very low misclassification rates (2.9–3.6%), suggesting that malignant and nonmalignant cell lines have distinct smell prints.8 With our study, we demonstrated, we believe for the first time, in the clinical setting (and not in cell lines) a potential role of the EN in identifying, by smelling urine samples, healthy patients (with negative PB) while maintaining an acceptable sensitivity. The overall agreement between biopsy and EN was 85% while the k value (0.66) indicates a good agreement between the two methods. The first part of the urine stream (and not the midstream) allowed to distinguish between positive and negative PBs, probably because it contains elements of prostatic secretion. The influence of eventual confounding factors that might alter the urine smell influencing the EN accuracy has been also evaluated. Alcohol, oral medications and smoke do not seem to influence the accuracy of the device. Overall, the results obtainable with the EN are chemical images with a rather high probability of cancer recognition. The high specificity of the device, if confirmed, could allow to reduce the & 2014 Macmillan Publishers Limited
Prostate cancer and electronic nose AD Asimakopoulos et al
211 number of first PBs or rebiopsies. It would appear that if the test results negative, the PB will be almost always (93%) negative; thus, both extra costs to the health-care system and potential complications to the patients of further diagnostic procedures may be spared. Two issues regarding the sensors should be further commented: their statistical contribution and their consistency with respect to prostate disease-alleged biomarkers. It is worth mentioning that the used EN consists of an array of non-selective gas sensors, thus each latent variable is a linear combination of all the sensors: they search for characteristic smell prints of particular conditions in the urine headspace without distinguishing the constituent VOCs. Over-fitting condition is avoided by the selection of the correct number of LVs (see Table 1). Moreover, as can be observed in the loading plots in Figure 6, none of the sensors in the array shows negligible load values with respect to the other ones. Concerning sensors’ pertinence with respect to PCa diagnosis, the possible discrimination of cancer is based on fingerprint differentiation, thus the volatile biomarkers of PCa remain to be identified, in order to use selective adsorbing material in sensor arrays. Some limitations of our study should be mentioned. The number of cases is small to permit definitive conclusions on the diagnostic accuracy of the EN, although the preliminary data appears at least promising. Only two cases of Gleason score 7 disease were included in the study, and none with Gleason scoreX8; consequently, the ability of the device in the diagnosis of high-grade PCa cannot be evaluated. However, it seems promising that both cases of Gleason 7 were correctly classified by the device as positive. The minimum number of positive cores/ minimum percentage of core involvement that correlates to an accurate diagnosis were not addressed. However, in our preliminary analysis, there was not a significant difference between mean number of positive cores in correctly identified PCa vs misclassified patients. Finally, any correlation between the EN results and the outcomes of the radical prostatectomy specimen was not explored. Table 3 suggests crucial areas for further future research on EN. CONCLUSIONS To the best of our knowledge, this is the first demonstration of an olfactory imprinting of the initial part of the urine stream in patients with PCa that was accurately revealed by an EN. The high specificity of the device, if confirmed in larger studies, may overcome the known specificity gap of the PSA serum test, and it may permit a more accurate selection of the PB candidates. CONFLICT OF INTEREST The authors declare no conflict of interest.
REFERENCES 1 Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin 2009; 59: 225–249. 2 Schro¨der FH. PSA screening—a review of recent studies. Eur J Cancer 2009; 45(Suppl 1): 402–404. 3 Stamey TA, Caldwell M, McNeal JE, Nolley R, Hemenez M, Downs J. The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? J Urol 2004; 172: 1297–1301. 4 Tuma RS. New tests for prostate cancer may be nearing the clinic. J Natl Cancer Inst 2010; 102: 752–754. 5 Matsumura K, Opiekun M, Oka H, Vachani A, Albelda SM, Yamazaki K et al. Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study using odor signatures in mouse models of lung cancer. PLoS One 2010; 5: e8819. 6 Cornu JN, Cancel-Tassin G, Ondet V, Girardet C, Cussenot O. Olfactory detection of prostate cancer by dogs sniffing urine: a step forward in early diagnosis. Eur Urol 2011; 59: 197–201.
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7 Lesniak A, Walczak M, Jezierski T, Sacharczuk M, Gawkowski M, Jaszczak K. Canine olfactory receptor gene polymorphism and its relation to odor detection performance by sniffer dogs. J Hered 2008; 99: 518–527. 8 Roine A, Tolvanen M, Sipila¨inen M, Kumpulainen P, Helenius MA, Lehtima¨ki T et al. Detection of smell print differences between nonmalignant and malignant prostate cells with an electronic nose. Future Oncol 2012; 8: 1157–1165. 9 Santonico M, Pennazza G, Capuano R, Falconi C, Vink TJ, Knobel HH et al. Electronic noses calibration procedure in the context of a multicentre medical study. Sens Actuators B Cheml 2012; 173: 555–561. 10 Sauerbrey G. Verwendung von Schwingquarzen zur Wa¨gung du¨nner Schichten und zur Mikrowa¨gung. Z Phys 1959; 155: 206. 11 Kneepkens CM, Lepage G, Roy CC. The potential of the hydrocarbon breath test as a measure of lipid peroxidation. Free Radic Biol Med 1994; 17: 127–160. 12 Bajaj A, Miranda OR, Kim IB, Phillips RL, Jerry DJ, Bunz UH et al. Detection and differentiation of normal, cancerous, and metastatic cells using nanoparticlepolymer sensor arrays. Proc Natl Acad Sci USA. 2009; 106: 10912–10916. 13 Filipiak W, Sponring A, Filipiak A, Ager C, Schubert J, Miekisch W et al. TD-GC-MS analysis of volatile metabolites of human lung cancer and normal cells in vitro. Cancer Epidemiol Biomarkers Prev 2010; 19: 182–195. 14 Williams H, Pembroke A. Sniffer dogs in the melanoma clinic? Lancet 1989; 1: 734. 15 Pickel D, Manucy GP, Walker DB, Hall SB, Walker JC. Evidence for canine olfactory detection of melanoma. Appl Anim Behav Sci 2004; 89: 107–116. 16 Ehmann R, Boedeker E, Friedrich U, Sagert J, Dippon J, Friedel G et al. Canine scent detection in the diagnosis of lung cancer: revisiting a puzzling phenomenon. Eur Respir J 2012; 39: 669–676. 17 Horvath G, Andersson H, Paulsson G. Characteristic odour in the blood reveals ovarian carcinoma. BMC Cancer 2010; 10: 643. 18 McCulloch M, Jezierski T, Broffman M, Hubbard A, Turner K, Janecki T. Diagnostic accuracy of canine scent detection in early- and late-stage lung and breast cancers. Integr Cancer Ther 2006; 5: 30–39. 19 Sonoda H, Kohnoe S, Yamazato T, Satoh Y, Morizono G, Shikata K et al. Colorectal cancer screening with odour material by canine scent detection. Gut 2011; 60: 814–819. 20 Willis CM, Church SM, Guest CM, Cook WA, McCarthy N, Bransbury AJ et al. Olfactory detection of human bladder cancer by dogs: proof of principle study. BMJ 2004; 329: 712. 21 Bijland LR, Bomers MK, Smulders YM. Smelling the diagnosis: a review on the use of scent in diagnosing disease. Neth J Med 2013; 71: 300–307. 22 Gordon RT, Schatz CB, Myers LJ, Kosty M, Gonczy C, Kroener J et al. The use of canines in the detection of human cancers. J Altern Complement Med 2008; 14: 61–67. 23 Leja MA, Liu H, Haick H. Breath testing: the future for digestive cancer detection. Expert Rev Gastroenterol Hepatol 2013; 7: 389–391. 24 http://www.cnr.it/documenti/Istituzionali/Consuntivo/Report/Report2003_file/pdf/ 237-238Focus.pdf. 25 Bajtarevic A, Ager C, Pienz M, Klieber M, Schwarz K, Ligor M et al. Noninvasive detection of lung cancer by analysis of exhaled breath. BMC Cancer 2009; 9: 348. 26 D’Amico A, Pennazza G, Santonico M, Martinelli E, Roscioni C, Galluccio G et al. An investigation on electronic nose diagnosis of lung cancer. Lung Cancer 2010; 68: 170–176. 27 Di Natale C, Macagnano A, Martinelli E, Paolesse R, D’Arcangelo G, Roscioni C et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 2003; 18: 1209–1218. 28 Machado RF, Laskowski D, Deffenderfer O, Burch T, Zheng S, Mazzone PJ et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 2005; 171: 1286–1291. 29 Barash O, Peled N, Hirsch FR, Haick H. Sniffing the unique "odor print" of nonsmall-cell lung cancer with gold nanoparticles. Small 2009; 5: 2618–2624. 30 Peled N, Hakim M, Bunn Jr PA, Miller YE, Kennedy TC, Mattei J et al. Non-invasive breath analysis of pulmonary nodules. J Thorac Oncol 2012; 7: 1528–1533. 31 Xu ZQ, Broza YY, Ionsecu R, Tisch U, Ding L, Liu H et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer 2013; 108: 941–950. 32 Horvath G, Chilo J, Lindblad T. Different volatile signals emitted by human ovarian carcinoma and healthy tissue. Future Oncol 2010; 6: 1043–1049. 33 Peng G, Hakim M, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br J Cancer 2010; 103: 542–551. 34 Phillips M, Cataneo RN, Saunders C, Hope P, Schmitt P, Wai J. Volatile biomarkers in the breath of women with breast cancer. J Breath Res 2010; 4: 026003. 35 D’Amico A, Bono R, Pennazza G, Santonico M, Mantini G, Bernabei M et al. Identification of melanoma with a gas sensor array. Skin Res Technol 2008; 14: 226–236. 36 Bernabei M, Pennazza G, Santonico M, Corsi C, Roscioni C, Paolesse R et al. A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sens Actuators B Chem 2008; 131: 1–4.
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