Lung Cancer Detection Using an Enose Ifprop

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Lung Cancer Detection Using an Electronic Nose

Anna Folinsky

California Institute of Technology

March 10, 2005

Abstract

Electronic nose systems have been used both in and out of laboratory settings fora wide variety of applications. One of the larger general initiatives is towards usingthem in biomedical applications, notably the detections of analytes which may becorrelated with disease states. This proposal outlines a system to detect biomarkersfound in the breath which are associated with lung cancer, one of the leading causesof death in the USA. Use of standard polymer/carbon black sensors from our lab willbe augmented with novel sensing technologies from our lab, in an effort to obtain thenecessary sensitivity and discriminatory power needed for the task. These should beable to differentiate diseased and healthy states, and potentially separate different stagecancer patients.

Introduction

Lung Cancer

It has long been known that certain diseases produce volatile compounds that can be smelled

on the breath, or elsewhere on the body. Two of the oldest, most common examples are the

scents of ketones on the breath of diabetic patients, and the smell of “freshly baked bread” on

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the skin of patients with typhoid. 1 More recently, GC studies have shown quantitatively that

many diseases produce distinct patterns of volatile compounds that could potentially be used

as biomarkers for these diseases 2–5 . Among these, lung cancer has been shown by several GC-

MS studies to display elevated levels of several volatile organic compounds (VOCs) on the

breath, mostly C 4 to C20 monomethylated alkanes, in addition to certain benzene derivatives

6,7 , although there is some variaton between studies as to precisely which compounds are

most indicative. On one occasion, Phillips et al identied styrene, 2-methylheptane, and

decane and three of the most important compounds for discrimination 8 ; in another, butane,

3-methyltridecane, and 4-methyloctane 2 were most informative.

Lung cancer is one of today’s leading health problems. It was the third leading cause

of death in the USA in 2002, behind only heart and cerebrovascular diseases. Estimates

indicate that 172,570 new cases will be reported in 2005, and 163,510 deaths from lung

cancer are expected the same year. The current 5-year survival rate for lung cancer is 15%,

but this rate rises to 49% if the cancer is discovered when it is still localized. Unfortunately,

only 16% of lung cancer cases are discovered while they are still localized 9 . There is clearly

a great need to improve early detection ability for this disease.

Bronchoscopy, biopsy, and sputum cytology are the current most common ways to di-

agnose lung cancer, but these methods can occasionally miss tumors, and are dependent

on tumor size 10 . There are also some reports of new methods for earlier detection, such

as uorescence bronchoscopy 11 , spiral CT scanning 12,13 , PCR sputum assays 14 , and using

computers to aid in the analysis of chest radiographs 12 . All of these, however, are expensive

and time consuming. A non invasive breath test would have great potential as a widespread

screen.

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Electronic Nose Systems

The vapor sensing method we pursue in our lab involves the use of arrays of sensors. No sensor

is designed to respond specically towards an individual compound. Instead, each sensor is

broadly responsive to a variety of odorants. Each analyte produces a distinct ngerprint from

the array of broadly cross-reactive sensors (Fig.1). Pattern recognition algorithms can then

be used to obtain information on the identity, properties, and concentration of the exposed

vapor 15–18 . In this respect, our system resembles that used in the mammalian olfactory

system, in which each olfactory receptor responds to a wide variety of odorants 19 , and our

array of sensors may be seen as analogous to the array of receptors in the nasal epithelium.

Due to this similarity, our system is sometimes designated as an “electronic nose”.

Figure 1: Differentiation between odorants: (a) an array of broadly-cross reactive sensorsin which each individual sensor responds to a variety of odors; (b) pattern of differentialresponses across the array produces a unique pattern for each odorant or odor.

A variety of signal transduction mechanisms have now been implemented to construct

electronic nose systems. Surface acoustic wave devices (SAWs) 20 , metal oxide sensors 21 ,

conducting organic polymers 22 , polymer coated quartz crystal microbalances (QCMs) 23 ,

polymer coated micro-machined cantilevers 24 , thin lm capacitors 25 , and polymer composite

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analyte vapors, demonstrated reproducibly over thousands of cycles over a period of months,

to a variety of organic vapors. The thin lms have been used to obtain response times in

essentially real time, with rise and fall times of less than 100ms in most cases and less than

20 ms in some systems27 (limited by the diffusivity of small molecules permeating through

low glass transition, rubbery, polymeric lms at room temperature).

This proposal will be centered upon this technology, to be augmented by newer sensor

substrates from our lab.

Proposed Studies

Rationale

This proposal will use the various sensor systems we have been developing to detect lung

cancer biomarkers in patients’ breath. GC studies showing that several VOCs were elevated

in the breath of lung cancer patients were able to use discriminant analysis on the alveolar

gradient of the GC-MS information to distinguish between healthy and cancerous patients

2,3 . Certain compounds of interest (styrene, decane, and isoprene among them) are found

at 1-20 parts per billion (ppb) in healthy breath, but are seen at levels from 10-100 ppb in

cancerous patients 28 .

Yu and coworkers used a GC column coupled with a SAW sensor and a neural network to

discriminate between healthy and diseased patients 29 , with preconcentration. Another study

used an array of coated QCM sensors, with no preconcentration 30 , to detect lung cancer in

patients prior to surgical tumor removal. They made no effort to quantify the analytes

detected, nor correlate them to stage of disease. However, they did show that the signature

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returned to a “healthy” pattern approximately one month after removal of the tumor in a

pair of cases, providing further evidence that the analytes are related to the disease.

Coated QCMs are comparable in sensitivity to SAW devices, however, carbon black /

polymer sensors have been shown to be more sensitive towards alkanes and related VOCs 31 ;

therefore the initial step of detection should be easily obtainable.

Our lab has already demonstrated the ability to detect and discriminate between all

the low molecular weight straight-chain alkanes, as seen in Fig. 3 and to quantify their

concentrations based on the amplitude of recorded patterns 32 . Separately, it has been shown

that patterns for alkanes and those for a variety of aromatics can be differentiated 33 . We

have also shown that even very similar alkane mixtures can be discriminated 26 . A binary

mixture of n-heptane at P/P o = 0.0011 (64 ppm) and n-hexane at P/P o = 0.00090 (192

ppm) has been differentiated from a binary mixture of n-heptane at P/P o = 0.00090 (58

ppm) and n-hexane at P/P o = 0.0011 (235 ppm) with 95% correct discrimination, using an

optimal subset of a chemiresistive detector array. Similarly, a mixture of 1-propanol at P/Po

= 0.0025(72 ppm) and 2-propanol at P/P o = 0.0025 (150 ppm) could be differentiated with

98% correct classication rate from a mixture of 2-propanol at P/P o = 0.0027 (164 ppm)

and 1-propanol at P/P o = 0.0023 (64 ppm).

Current Methods

Human breath is composed mostly of water vapor, with all other analytes existing only

as minor substituents. This fact implies the need to deal not only with the low levels

of the analytes of interest but also with the high background level of water. While the

level of water is considered a primary obstacle for commercial sensor arrays, whose polar,

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Figure 3: Principal components data from a 20-detector array exposed 5 times to of thelabeled analytes, each at 0.005 - 0.03 P/P o , containing 99% of the total variance. Theellipsoids contain 99% of the data for each analyte. All presentations were in each setrandomized over all repetitions

inherently conducting polymer sensors (made from inherently conductive materials, such

as polyaniline, polypyrrole, polythiophene, etc) are highly sensitive to water vapor, our

approach of using composites of inorganic conductors and sorptive insulating organic phases

allows development of chemiresistive sensors that are relatively insensitive to water vapor 19 .

There are also a pair of newer sensing substrates being tested, expected to yield improved

sensor classication. The rst of these is based on composites of homogenous or blended

organic nonvolatile molecules with conductors such as carbon black 34 . These sensors have

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been constructed from either pure compounds or mixtures of moderate length monomeric

organic molecules used as binders, mixed with carbon black. The sensors show fast response

time, good reversability, and high stability. Furthermore, they show the ability to discrimi-

nate and classify both similar and different types of analytes, even at low concentrations in

air, compared to similar sensors based on polymer binders. They also allow an even broader

choice of substrates, and also allow us to achieve a higher density of functional groups in the

thin lms than we are allowed by using the polymer composites. Principal component anal-

ysis of the sensor array responses showed clear distinctions between different types of vapors

allowing easy classication. This approach is particularly applicable to the development of

biosensors where the ability to detect low concentrations of specic types of compounds is

required.

The second approach uses ligand-capped Au nanoparticles. Thiol capped gold nanopar-

ticles in the range of 2-10 nm have been synthesized and tested as sorption based detectors

for different analytes35

. Thiols investigated varied by chain length, polarity, and functional

group. Effects of chemical functionalization of the thiol on sensor sensitivity , specicity, and

stability were studied. Sensor specicity varied with the thiol functional group. Most thio-

late gold nanoparticles chemiresistors showed good stability over three months, with longer

chain length thiols having better stability than short chain lengths, and a high ability to

classify and discriminate analytes according to their polarity and vapor pressure. In specic

cases, the selectivity and discrimination of such arrays were superior to arrays of carbon

black/polymer composite sensors.

Recent work in our lab has shown that such sensors can differentiate between mixtures

of long chain alkanes, at very low concentrations, in the presence of saturated water va-

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por. Two mixtures of nonane and hexadecane were tested in background ows of saturated

water vapor at room temperature, to roughly simulate human breath (human breath is at

34.5o C saturated water vapor; however, the difference is minor for these demonstration

purposes). Standard exposures consisted of a background stream of water-vapor saturated

air which contained one of two predetermined mixtures of nonane and hexadecane. The

low concentration mixture had nonane at P/P o = 0.0010 (4 ppm) and hexadecane at P/P o

= 0.010 (40 ppb) in saturated water vapor, and was representative of the key alkane VOC

components of a healthy patient’s breath, while a higher concentration mixture of nonane

at P/P o = 0.0050 (20 ppm) and hexadecane at P/P o = 0.050 (200 ppb) in saturated water

vapor was used to be representative of the key elevated concentration VOC components that

have been reported as diagnostic of lung cancer. Sixty exposures to each breath type were

performed at ows of 5 L/min and data were obtained for carbon black/polymer compos-

ites, carbon black/monomer composites, and ligand capped Au nanoparticles. Other than

selection for sensors we knew empirically to provide generally good responses, the sensors

were not optimized for the task.

Figure 4 presents the principal component plots for the normalized data of the three sen-

sor classes used in the preliminary experiment. The principal component plots are simply

transformations of the multivariate responses of the sensor array into orthogonal directions

which statistically capture the most variance between the data, and therefore allow conve-

nient visualization of the differentiation ability of the sensor array for the analytes of interest,

through a reduction in dimensionality of the data. As shown in Fig 4, discrimination between

“cancerous” and “healthy” breath was possible with each class of sensor arrays. The best

discrimination ability was exhibited by the carbon black/monomer composite sensors, which

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Figure 4: Principal componenent plots indicating the ability to distinguish between “healthy”

and “cancerous” patients. (a) 6 sensors of poly(vinylstearate),poly(ethylene-co-vinyl ac-etate), and poly(ethylene vinyl alcohol), all 40 wt% carbon black (CB). (b) 4 sensors of 2-5nm Au colloids, capped with hexanethiol or 6-mercapto-1-hexanol. (c) 8 sensors of lauricacid, tetracosane, and tetracosanoic acid, with 30 wt% dioctyl phthalate, and tetracosanoicacid, all with 75 wt% CB

can be attributed either to the larger number of those sensors, or to the unique properties

of these sensors. For the other two classes, increasing the diversity of chemiresistors (in an

array of sensors) and modifying the physical properties of their building blocks is expectedto give better discrimination. Since our sensors operate solely by detecting changes, we will

be able to detect these analytes even in the presence of a constant background of other

components of an analyte mixture (as has been previously demonstrated in a variety of sit-

uations for nerve agent simulants at low concentrations in the presence of many different

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background ambients 36 ). This, and the prior data, demonstrate the ability of our sensors to

discriminate between levels, and mixtures, of straight chain alkanes that have been reported

to be signatures of lung cancer in breath, even in the presence of saturated water vapor, and

without preconcentration.

Experimental

We will receive 4 L samples of lung air in inert Tedlar bags from our clinical partner, Dr.

David E. McCune, Chief, Clinical Studies Service, and Director of Clinical Trials at the

Madigan Army Medical Center, Tacoma, Washington. The Army is one of the largest health-

care providers in the USA, and this could facilitate ready expansion of the number of testing

sites if such is warranted further in the study. Vapor sampling will be done by extended

breath sampling, in which the patient will breathe into the collection apparatus for 10-30

min. The rst two minutes of breath sample will be discarded, due to likely contamination

of upper respiratory air. The later deep lung air will be retained for testing purposes. These

samples will be collected with a straw, or other suitable tube in the patient’s mouth, that is

connected to the collection bag. These subjects will be diverse, and background information

will be obtained from patients regarding such factors as age, sex, race, smoking history, etc,

which will allow us both to provide for diversity in our data, and also to match each patient

with a control subject. We will take these samples back to Caltech and ow them into a

home-made chamber containing our sensors, at a relatively slow ow rate (approx. 100-300

mL/min), to obtain sensor response data, and will also use these samples in a parallel GC-MS

study.

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The targeted VOCs are diverse enough that comparison studies will be needed to validate

the results we will obtain with our chemiresistor sensor arrays. We will do this via a GC

study in parallel with acquiring the sensor response data. A portion of each sample will be

retained for use with our GC-MS system, coupled with an automatic headspace sampler. As

standard headspace/gas sampling techniques have sensitivities only in the ppt-ppm range 37 ,

we will use purge and trap thermal desorption or solid phase micro-extraction methods to

allow GC-MS to attain the necessary ppb detection levels to provide the information on the

VOC level and composition of our samples.

We will run several parallel sensor studies to both validate the responses and to optimize

the testing method. The initial method will involve owing the as-received samples through

our sensor chambers and a comparison of the breath sample results with the response to

a clean background of laboratory air. We will only use sensors having minor sensitivity to

water (as those used in Fig. 4). Additionally, in parallel analyses, we will remove the water

from the samples by running the analyte ow through a desiccant chamber before presenting

the ow to the sensors. Both methods will be compared critically for their performance

with the breath samples. Preconcentration will also be used on the samples to produce

higher concentrations of the VOCs to be analyzed. Another key step will be to create

articially the VOC sample concentrations indicated by the GC-MS, most likely through

successive dilutions of the saturated vapor phase of the pure VOCs, in order to create the

extremely low concentration levels needed. That mixture will then be exposed to the sensors

to determine whether the breath sample response pattern is indeed due to the VOCs detected

by the GC-MS method, or whether the sensor pattern arised from different volatile breath

biomarkers. The rst set of samples will be taken from patients with known conditions, to

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allow us to calibrate and perfect the sampling and signal processing methods. Subsequent

samples will be analyzed by personnel who are blind as to whether the patient has lung

cancer (as diagnosed by conventional methods) or not.

Ideally, we would be able to use this data to also classify patients on the basis of stage

of their cancer. This would clearly be a great aid in diagnosing lung cancer at the clinically

more useful earlier stages. Some information suggests this may not be possible, however.

One study suggests that the elevated VOC levels are comparable in all stages of the cancer,

because they are not associated with the size of the cancerous tumor, but instead are a

signature of oxidative stress related to a change in body chemistry as a result of development

of the cancerous condition 2 . With ndings consistent with this hypothesis, a study in India

was performed on 108 patients with abnormal chest radiographs who were scheduled for

bronchoscopy 38 . The breath of these patients was assayed by GC-MS. Lung cancer was

conrmed histologically in 60 patients. A combination of 22 VOCs, predominantly alkanes,

alkane derivatives, and benzene derivatives, discriminated between patients with and without

lung cancer, regardless of stage (all p < 0.00003). For stage 1 (initial) 39 lung cancer, a

combination of 22 VOCs had 100% sensitivity and 81.3% specicity. Cross validation of the

combination correctly predicted the diagnosis in 71.7% of the patients with lung cancer and

66.7% of those without lung disease.

Sensors with good shelf life and homogeneous response can be fabricated in batch form in

our lab. When using spray coating, batches of sensors typically have ≤ 10% difference in their

baseline resistances and ≤ 15% variation in their response during a usage period over several

months 19 . Data analysis will be performed initially using standard chemometric methods

such as principal components analysis and linear discriminant analysis, which are easily

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implemented using commercial software packages, such as MATLAB, with algorithms that

already exist and have been extensively previously used in our lab. Although these methods

have proven to be quite useful for analyte discrimination, more sophisticated algorithms

(e.g. supervised or unsupervised neural networks, or a variety of non-linear methods) will

be employed if necessary. However, if, as suggested, the biomarkers are entirely due to static

levels of production due to oxidative stress, that is also of great interest. While we won’t

be able to differentiate, we will still be able to detect at early stages, which would still be

enormously benecial as an early detection screen.

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Lung Cancer Detection Using an Enose Ifprop