Supplemental Online Material for
A Nanomaterial-based Breath Test for Distinguishing
Gastric Cancer from Benign Gastric Conditions
Zhen-qin Xu1,2‡, Yoav Y. Broza1,‡, Radu Ionsecu1,‡, Ulrike Tisch1, Lu Ding2, Hu
Liu*2, Qi Song2, Yue-yin Pan2, Fu-xin Xiong2, Kang-sheng Gu2, Guo-ping Sun2,
Zhen-dong Chen2, Marcis Leja3, and Hossam Haick*1
1
Department of Chemical Engineering and Russell Berrie Nanotechnology Institute,
Technion–Israel Institute of Technology, Haifa 3200003, Israel
2
Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei
230032, China.
3
Department of Reasearch, Riga East Clinical University Hospital, University of Latvia,
Digestive Diseases Centre GASTRO, 6 Linezera Iela, LV1006 Riga, Latvia
‡
These authors have equal contribution to the manuscript
*Correspondence to:
Prof. Hossam Haick: the Department of Chemical Engineering and Russell Berrie
Nanotechnology Institute, Technion – Israel Institute of Technology, Haifa 3200003, Israel.
Tel: +972(4)8293087; Fax: +972(4)8295672; Email: hhossam@technion.ac.il
Prof. Hu Liu: Department of Oncology, The First Affiliated Hospital of Anhui Medical
University, Hefei 230022, China. Fax:+86 551 2922987 ; Email: drliuhu@gmail.com
1
S1 Extended Methods
S1.1 Breath Collection, Sample Preparation and Storage
Exhaled alveolar breath was collected in a controlled manner and in the same clinical
environment from each volunteer. The inhaled air was cleared of ambient
contaminants by a lung washout, during which the patient inhaled repeatedly to total
lung capacity for 3 min through a mouthpiece with a filter cartridge on the inspiratory
port mouthpiece (purchased from Eco Medics, Duerten, Switzerland), thus greatly
reducing the concentration of exogenous VOCs (Peng et al, 2010; Peng et al, 2009).
All breath samples were collected in the same clinical environment. A reference
sample of similarly filtered hospital air was sampled in the morning of each collection
day, in order to identify the hospital contaminations that might be present in high
concentration at the collection site, and to monitor their concentration levels.
Following the lung washout, subjects inhaled to full lung capacity and exhaled slowly
through the mouthpiece into a separate exhalation port against 10–15 cm H2O
pressure. This ensured the closure of the vellum in order to exclude contamination
through nasal entrainment. Exhaled breath consists of respiratory dead space air that
is exhaled first, followed by the alveolar air from the lungs. At the beginning of the
patients’ breath exhalation, the dead space air was automatically filled into a
designated dead space bag and later removed. The alveolar breath from the end of the
exhalation was filled into a 4L Tedlar® bag (Keika Ventures, LLC). It should be
emphasized that the described breath collection is a single-step process that does not
require the volunteer to take care of changing between the dead space and alveolar
breath bags. Two bags were collected per test person for the analysis with gaschromatography/mass-spectrometry (GC-MS) and with the nanomaterial based
2
sensors – see below. Immediately after the breath collection, the VOCs in the breath
samples were trapped and pre-concentrated in two-bed ORBOTM 420 Tenax® TA
sorption tubes for gas and vapor sampling (specially treated; 35/60 mesh; 100/50 mg;
purchased from Sigma-Aldrich, USA) by pumping the content of each collection bag
through a sorbent tube for 20 min. (flow rate: 200 ml/min.). The room air samples
were collected by pumping ambient air in the collection room through a sorbent tube
for 20 min. at a rate of 200 ml/min. Note that Tenax TA showed low water trapping of
generally less than ∼2-3 mg of water/g of adsorbent even at 100% relative humidity at
room temperature (Helmig & Vierling, 1995). This is an important feature, because
exhaled breath is composed mainly of nitrogen, oxygen, carbon dioxide, water vapor,
and inert gases (Amann et al, 2010; Amann et al, 2007). The VOCs that are generated
by the cellular biochemical processes of the body are present in much lower amounts
in exhaled breath, and many diseases manifest themselves through very subtle
changes in concentration of a multitude of these breath VOCs (Amann et al, 2010;
Amann et al, 2007; Tisch & Haick, 2011).
When 4l of breath sample or room air is pumped through Tenax traps,
breakthrough could be an issue. However, breakthrough depends on the amount of
absorbent material and the substance in hand. The two-bed ORBOTM 420 Tenax® TA
sorption tubes were constructed in two beds as backup to handle breakthrough.
Additionally breakthrough volumes for Tenax TA at the given conditions were more
than 26 liter per gram of resin, according to the information of Sigma-Aldrich, China.
Therefore most VOCs should not be influenced by breakthrough. Moreover VOCs
with a high tendency to breakthrough were removed (e.g. ethanol, pentene) during the
data analysis.
3
The sorbent tubes were stored under refrigeration at 4°C, until they were
transported (under refrigeration) in a single shipment to the laboratory facilities for
the breath analysis (Laboratory for Nanomaterial-Based Devices, Technion, Israel).
The maximal duration between breath collection and sample analysis was 4 months.
We have verified in a separate experiment that selected typical VOCs that have been
observed in the breath samples of lung cancer patients (decane, benzene, aldehydes
and branched aldehydes) (Hakim et al, 2012) can be stored in the ORBOTM 420
Tenax® TA sorption tubes for at least 6 months, if the tubes are properly sealed and
continuously cooled. Note that currently the study of cancer biomarkers in exhaled
breath suffers from a lack of standardization of the breath collection and analysis.
Amann and co-workers have recently proposed a standardization of the breath
collection process that might be generally accepted in the future (Amann et al, 2010).
S1.2 Breath Sample Analysis with Gas-Chromatography/Mass-Spectrometry
The VOCs in the breath samples were identified using a GCMS-QP2010 instrument
(Shimadzu Corporations) with a SLB-5ms capillary column (with 5% phenyl methyl
siloxane; 30 m length; 0.25 mm internal diameter; 0.5 μm thicknesses; from SigmaAldrich), combined with a thermal desorption (TD) system (TD20; Shimadzu
Corporation). Immediately prior to the analysis, the breath VOCs were transferred
from the ORBOTM 420 Tenax® TA sorption tubes (one of the two tubes per patient) to
pre-conditioned Tenax® TA / Carboxen™ 1018 glass TD tubes (from Sigma-Aldrich)
that are compatible with the TD system: at first the VOCs were released from the
Tenax sorbent material of each storage tube by heating the sorbent material for 10
min. at 270°C in a pre-heated stainless steel thermal desorption chamber (350 ml);
then the air with the breath VOCs from the chamber was pumped through a TD tube
4
(flow rate: 100 ml/min). The TD tubes were injected into the GC-system in splitless
mode at 30 cm/sec constant linear speed and under 0.70 ml/min column flow. The
following oven temperature profile was set: (a) 10 min at 35°C; (b) 4°C/min ramp
until 150°C; (c) 10°C/min ramp until 300°C; and (d)15 min at 300°C. The GC-MS
chromatograms were analyzed using the GCMS post-run analysis program (GCMS
solutions version 2.53SU1, Shimadzu Corporation), and the compounds were
tentatively identified through spectral library match (Compounds library of the
National Institute of Standards and Technology, Gaithersburg, MD 20899-1070
USA). The identity of the compounds was confirmed and quantification were
achieved through measurements of external standards (Tetra-chlorobutyl acetate, 2Propenenitrile, 1-Methoxy-2-propanol, 2-Butoxy-ethanol, 2-Butoxy-ethanol, Furfural,
2-Pentyl acetate, 6-Methyl-5-hepten-2-one, Isoprene, 2-Phenoxy-ethanol and 1Pentene, all purchased from Sigma- Aldrich, Israel). The gaseous standards were
produced using a commercial permeation/diffusion tube dilution (PDTD) system
(Umwelttechnik MCZ, Germany). Purified dry nitrogen (99.9999%) from a
commercial nitrogen generator (N-30, On Site Gas Systems, USA) equipped with a
nitrogen purifier was used as a carrier gas. The PDTD system used a temperature
controlled oven to mix a constant flow (200 ± 1 cm3/min) of purified nitrogen with a
constant mass flow of vaporized VOC(s) exiting a diffusion tube (Dynacal, VICI
Metronics). The nitrogen/VOC mixture exiting the PDTD system was diluted again
with N2 to achieve the desired concentrations in the range from single ppbv to several
ppmv. The VOC concentration was determined by controlling the mass flow rate of
the vaporized VOC(s) (via the temperature of the diffusion tubes) and the total
volumetric nitrogen flow rate. Four liters of each calibration gas mixture were
pumped through an ORBOTM 420 Tenax® TA sorption tubes at a rate of 200ml/min.
5
The calibration samples were analyzed under the same experimental conditions as the
breath samples.
The following typical hospital contaminants (Amann et al, 2010) were found
in the room air samples at the location of the breath tests that were taken on each
collection day, and were subsequently discharged from the analysis: propanol, ethanol
and methyl-isobutyl-ketone. Note that these substances were found only in low
concentration in less than 30% of the study population, due to efficient lung washout
with filtered air prior to the breath test (see section S1.1). Contaminants of the Tenax
sorbent material were identified through GC-MS analysis of pristine Tenax material
from unused ORBOTM 420 Tenax® TA sorption tubes.
Statistical analysis was carried out using SAS JMP, Version 8.0 (SAS Institute
Inc., Cary, NC, USA, 1989-2005) for Wilcoxon/Kruskal-Wallis tests.
S1.3 Breath Sample Analysis with the Nanomaterial-Based Sensor Array
The Tenax TA sorbent material from the second ORBOTM 420 Tenax® TA sorption
tube per patient was heated at 270°C for 10 min. in a pre-heated 750 ml stainless steel
TD chamber to release the breath VOCs. Pulses of the breath sample from the TD
chamber were then delivered by a gas sampling system into a stainless steel test
chamber containing the array of 14 cross reactive nanomaterial-based sensors that is
described below in section S1.4. The test chamber was evacuated between exposures
to release the VOCs that the sensors adsorbed. An Agilent Multifunction switch
34980 was used to measure the resistance of all the sensors simultaneously as a
function of time. The sensors’ baseline responses were recorded for 5 min in vacuum,
followed by 5 min under breath sample exposure, followed by another 5 min in
vacuum. In order to detect possible malfunctions of the sensors, and to counteract
6
slight drifts of their baseline conditions due to ageing and/or poisoning effects, the
sensors were calibrated daily, by exposing the sensors to known concentrations of two
calibration compounds and recording their resistance changes. The following
calibration procedure was used: evacuation of the test chamber for 5 min in order to
eliminate possible contaminations, followed by exposure for 5 min to 44 ppm of ethyl
benzene (as first calibration reference), followed by exposure for 5 min to 3ppm of 2ethyl hexanol (as second calibration reference), and concluded evacuation of the test
chamber for 5 min in order to eliminate the calibration compounds from the test
chamber.
The exposure of the GNP sensors to the breath samples or the calibration
compounds resulted in rapid and fully reversible changes of the electrical resistance.
Four sensing features were read out from the time-dependent resistance response of
each sensor that related to the normalized resistance change at the beginning of the
exposure, at the middle of the exposure and at the end of the exposure (with respect to
the value of sensors resistance in vacuum prior to the exposure), and to the area
beneath the time-dependent resistance response during the last third of the exposure
period. The net sensing features that were extracted for the breath samples were then
divided by the corresponding values that were obtained for the reference calibration
compound.
Each sensor responded to all (or to a certain subset) of the VOCs found in the
exhaled breath samples. Breath patterns were obtained from the collective response of
the sensors by applying Discriminant Factor Analysis (DFA) as statistical pattern
recognition algorithm. DFA was also used as a heuristic to select the sensors with the
most relevant organic functionality out of the repertoire of fourteen, by filtering out
non-contributing sensors. The reason for selecting a certain set of sensing features for
7
a particular problem is directly derived from their ability to discriminate between the
various classification groups.
S1.4 Description of the Nanomaterial-Based Sensor Array
The nanomaterial-based sensor array that was used to analyze the breath samples
contained cross-reactive, chemically diverse chemiresistors that were based on two
types of nanomaterials: (i) organically stabilized spherical gold nanoparticles (GNPs,
core diameter: 3-4 nm), and (ii) single walled carbon nanotubes (SWCNTs) (see
Table S1). The chemical diversity of the sensors was achieved through fourteen
different organic functionalities (ten for the GNP sensors and four for the SWCNT
sensors) that are listed in Table S1. The organic ligands of the GNPs provided broadly
cross-selective absorption sites for the breath VOCs (Peng et al, 2010; Peng et al,
2009; Tisch & Haick, 2010).
The GNPs were synthesized as described in Refs. (Dovgolevsky et al, 2010;
Dovgolevsky & Haick, 2008; Dovgolevsky et al, 2009; Peng et al, 2009; Tisch &
Haick, 2010) and dispersed in chloroform. Chemiresistive layers were formed by
drop-casting the solution onto semi-circular microelectronic transducers, until a
resistance of several Mwas reached. The device was dried for 2 h at ambient
temperature and then baked overnight at 50°C in a vacuum oven. The microelectronic
transducers consisted of ten pairs of circular interdigitated (ID) gold electrodes on
silicon with 300 nm thermal oxide (Silicon Quest International, Nevada, US). The
outer diameter of the circular electrode area was 3mm, and the gap between two
adjacent electrodes and the width of each electrode both 20 m.
The SWCNT sensors were based on electrically continuous random networks
of SWCNTs that were formed by drop-casting a solution of SWCNTs (from ARRY
International LTD, Germany; ∼30% metallic, ∼70% semiconducting, average
8
diameter = 1.5 nm, length = 7 mm) in dimethylformamide (DMF, from Sigma Aldrich
Ltd., >98% purity) onto the pre-prepared electrical transducers. After the deposition,
the devices were slowly dried overnight under ambient conditions to enhance the selfassembly of the SWCNTs and to evaporate the solvent. The procedure was repeated
until a resistance of 100 KΩ to 10 MΩ was obtained. The microelectronic transducers
for the SWCNT sensors consisted of ten pairs of 4.5 mm wide, interdigitated Ti/Pd
electrodes on silicon with 2wo microns of thermal oxide (Silicon Quest International,
Nevada, US). The gap between two adjacent electrodes was 100 m. The SWCNT
sensors were organically functionalized with cap-layers that were composed of two
Polycyclic Aromatic Hydrocarbon (PAH) derivatives and two Hexabenzocoronene
(HBC) derivatives. PAH-5 and PHA-6 contain hydrophobic mesogens that are
terminated with alkyl chains and functionalized with alcohol and carboxylic acid,
respectively (Zilberman et al, 2011). HBC-C12 and HBC-C6,2 have discotic aromatic
cores and a corona composed of straight C12 and branched C6,2 alkyl chains
(Zilberman et al, 2009; Zilberman et al, 2010).
The GNP and SWCNT/PAH or SWCNT/HBC sensors used in this study
responded rapidly and reversibly when exposed to typical VOCs in the breath (Peng
et al, 2008; Zilberman et al, 2011; Zilberman et al, 2010). Additionally, we have
confirmed that they have a very low response to water (Konvalina & Haick, 2011;
Peng et al, 2009; Zilberman et al, 2011; Zilberman et al, 2009; Zilberman et al,
2010). However, in this study we used Tenax TA sorbet material to trap the breath
samples, which shows low water trapping of generally less than ∼2-3 mg of water/g
of adsorbent even at 100% relative humidity at room temperature (Helmig & Vierling,
1995). Therefore the effect of the high and varying room air humidity and the high
and varying humidity levels in exhaled breath is insignificant. We have carefully
9
verified in a separate experiment (using GC-MS) that the humidity levels of the
samples obtained from ORBOTM 420 Tenax® TA sorption tubes is negligible.
GNPs
35*
Decanethiol
x
x
2-Ethylhexanethiol
x
x
2-Nitro-4-trifluoro- methylbenzenethiol
x
x
Octadecanethiol
x
x
93
130
Endoscopic abnormalities
without ulceration (29)No endoscopic
abnormalities (32)
130
GC (37) - Gastric ulcer
(32)- Less severe gastric
conditions (61)
Early-stage GC (17) Late-stage GC (18)
Total number of independent
measurements per DFA model §
Gastric ulcer (32) – Less
severe gastric conditions
(61)
Organic Functionality
GC (37) – Non-malignant
gastric conditions (93)
Base Material
Table S1 The 14 organic functionalities of the nanomaterial-based sensors in the
array. The sensors that were selected for the different DFA models are marked by (x).
61
x
x
x
Tert-dodecanethiol
x
x
2-Amino-4-chlorobenzenethiol
x
x
2-Mercaptobenzimidazole
x
x
x
3-Ethoxythiophenol
x
x
x
2-Naphthalenethiol
x
x
2-Ethylhexyl-3-mercaptopropionate
SWCNTs
Benzyl Disulfide
PAH-5
x
PAH-6
x
HBC-C12
x
HBC-C6,2
x
x
x
x
GNPs: Gold nanoparticles; SWCNTs: Single walled carbon nanotubes; PAH: Polycyclic Aromatic Hydrocarbon;
HBC: Hexabenzocoronene; * Note that two of the 37 GC patients were excluded, because no staging information
was available for them.§ i.e. total number of samples (one sample per patient)
10
S2 Additional Data Analysis
3
Endoscopic abnormalities without ulceration
No endoscopic abnormalities
2
CV 2
1
0
-1
-2
-4
-3
-2
-1
0
1
2
3
4
CV 1
Figure S1 Distinction between patients showing endoscopic abnormalities without ulceration
(that are associated with gastritis according to the Sidney classification) from patients with no
obvious endoscopic abnormalities of the stomach lining.
11
REFERENCES:
Amann A, Miekisch W, Pleil J, Risby T, Schubert J (2010) Chapter 7: Methodological issues
of sample collection and analysis of exhaled breath European Respiratory Society Monograph
49: 96-114
Amann A, Spanel P, Smith D (2007) Breath analysis: the approach towards clinical
applications. Mini-Rev Med Chem 7(2): 115-129
Dovgolevsky E, Konvalina G, Tisch U, Haick H (2010) Monolayer-capped cubic platinum
nanoparticles for sensing nonpolar analytes in highly humid atmospheres. J Phys Chem C
114(33): 14042–14049
Dovgolevsky E, Haick H (2008) Direct observation of the transition point between quasispherical and cubic nanoparticles in two-step seed-mediated growth method. Small 4(11):
2059-2066
Dovgolevsky E, Tisch U, Haick H (2009) Chemically sensitive resistors based on monolayercapped cubic nanoparticles: towards configurable nanoporous sensors. Small (Weinheim an
der Bergstrasse, Germany) 5(10): 1158-1161
Hakim M, Broza YY, Barash O, Peled N, Phillips M, Amann A, Haick H (2012) Volatile
Organic Compounds of Lung Cancer and Possible Biochemical Pathways. Chem Rev
112(11): 5949-5966
Helmig D, Vierling L (1995) Water Adsorption Capacity of the Solid Adsorbents Tenax TA,
Tenax GR, Carbotrap, Carbotrap C, Carbosieve SIII, and Carboxen 569 and Water
Management Techniques for the Atmospheric Sampling of Volatile Organic Trace Gases.
Anal Chem 67(23): 4380-4386
Konvalina G, Haick H (2012) Effect of humidity on nanoparticle-based chemiresistors: a
comparison between synthetic and real-world samples. ACS Appl Mater Interfaces 4(1): 317325.
Peng G, Hakim M, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A, Tisch U, Haick H
(2010) Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a
single array of nanosensors. Br J Cancer 103: 542-551
Peng G, Tisch U, Adams O, Hakim M, Shehada N, Broza YY, Billan S, Abdah-Bortnyak R,
Kuten A, Haick H (2009) Diagnosing lung cancer in exhaled breath using gold nanoparticles.
Nature Nanotechnol 4(10): 669-673
Peng G, Trock E, Haick H (2008) Detecting simulated patterns of lung cancer biomarkers by
random network of single-walled carbon nanotubes coated with nonpolymeric organic
materials. Nano Lett 8(11): 3631-3635
Tisch U, Haick H (2010) Sensors based on monolayer-capped metal nanoparticles. In
Chemical Sensors - Vol. 2: Nanstructured Materials, Korotcenkov G (ed) Vol. 2
(Nanstructured Materials), Chapter 4, pp 141-202.
Tisch U, Haick H (2011) Arrays of chemisensitive monolayer-capped metallic nanoparticles
for diagnostic breath testing. Rev Chem Eng 26: 171-179
Zilberman Y, Ionescu R, Feng X, Müllen K, Haick H (2011) Nanoarray of polycyclic
aromatic hydrocarbons and carbon nanotubes for accurate and predictive detection in realworld environmental humidity ACS Nano 5(8): 6743-6753
12
Zilberman Y, Tisch U, Pisula W, Feng X, Müllen K, Haick H (2009) Sponge-like structures
of Hexa-peri-hexabenzocoronenes derivatives enhances the sensitivity of chemiresistive
carbon nanotubes to nonpolar volatile organic compounds. Langmuir 25(9): 5411–5416
Zilberman Y, Tisch U, Shuster G, Pisula W, Feng X, Müllen K, Haick H (2010) Carbon
nanotube/Hexa-peri-hexabenzocoronene bilayers for discrimination between nonpolar volatile
organic compounds of cancer and humid atmospheres. Adv Mater 22: 4317-4320
13