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Exhaled Breath Markers for Non-Imaging and NonInvasive Measures for Detection of Multiple Sclerosis Yoav Y. Broza, Lior Har-Shai, Raneen Jeries, John C. Cancilla, Lea Glass-Marmor, Izabella Lejbkowicz, José S. Torrecilla, Xuelin Yao, Xinliang Feng, Akimitsu Narita, Klaus Müllen, Ariel Miller, and Hossam Haick ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.7b00181 • Publication Date (Web): 02 Aug 2017 Downloaded from http://pubs.acs.org on August 3, 2017

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ACS Chemical Neuroscience

Exhaled Breath Markers for Non-Imaging and Non-Invasive Measures for Detection of Multiple Sclerosis

Yoav Y Broza1,‡, Lior Har-Shai2,3,‡, Raneen Jeries1,‡, John C. Cancilla4, Lea GlassMarmor2,3, Izabella Lejbkowicz2,3, José S. Torrecilla4, Xuelin Yao5, Xinliang Feng5, Akimitsu Narita5, Klaus Müllen5, Ariel Miller2,3, and Hossam Haick*,1 1

2

3

4

5 ‡

Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion – Israel Institute of Technology, Haifa 32000003, Israel Division of Neuroimmunology and Multiple Sclerosis Center, Carmel Medical Center, Haifa 34362, Israel. Pharmacogenetics & Personalized Medicine Center, Rappaport Faculty of Medicine & Research Institute, Technion-Israel Institute of Technology, Haifa 31096, Israel Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040-Madrid, Spain. Max Planck Institute for Polymer Research, Ackermannweg 10, D-55128 Mainz, Germany Authors have equal contribution to the manuscript

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ABSTRACT Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain and/or spinal cord. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease progression. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on non-invasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 volunteers, 164 MS and 58 control individuals. Analysis was performed by: gaschromatography mass-spectrometry (GC-MS) and nanomaterial-based sensors array. Predictive models were derived from the sensors, using Artificial Neural Networks (ANNs). GC-MS analysis revealed significant differences in VOCs abundance between MS patients and Controls. Sensor data analysis on training sets were able to binary discriminate between MS patients and Controls with accuracies up-to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control and 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.

Keywords: Multiple sclerosis; breath; diagnosis; volatile organic compound; sensor; spectrometry.


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INTRODUCTION Multiple Sclerosis (MS) is the most common chronic neurological disease affecting young adults, with usually onset at the age of 20-50 years old, and is more frequent in women (about 3 times more than in men).(1, 2) The diagnosis of MS is based on clinical characteristics. History and physical examination are the most significant factors in determining the diagnosis of the disease, but there are several ancillary laboratory tests which assist the clinician in establishing the diagnosis.(1, 2) The most commonly employed tool for confirmation of MS diagnosis is Magnetic Resonance Imaging (MRI) of brain and/or spinal cord.(1, 3) MRI is considered the test-of-choice to support the clinical diagnosis of MS. According to McDonald's diagnostic criteria, demonstration of characteristic lesions dissemination in time and space is necessary to establish the diagnosis.(1) Characteristic lesions on MRI are found in 70-95% of patients diagnosed with MS.(4) Most lesions detected on MRI correlate with MS pathologic lesions. However, in some cases, detected lesions that are extensive on MRI show only small plaques on pathological examination.(5) Furthermore, Central nervous system (CNS) lesions which are related to other etiologies (e.g., ischemia, Systemic lupus erythematosus (SLE), Behcet's, etc.) can appear very similar to MS lesions on MRI, making them difficult to distinguish from one another.(6) Moreover, MRI is an expensive procedure that is not accessible in routine medical care. Another ancillary test is performed by analyzing the levels of IgG Oligoclonal Bands (OCBs), extracted from cerebrospinal fluids (CSF), by means of electrophoresis. This test involves performing a lumbar puncture, which is an invasive procedure, accompanied by pain and discomfort to the patient and potentially infectious and can lead to neurological complications.(7, 8) The test is positive in 85-



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95% of patients with a clinical diagnosis of MS.(9) However, OCBs test results were found positive in up to 8% of CSF samples from non-MS subjects.(4, 10) Additionally, due to its invasiveness, it is not an option for repeated sampling as part of routine follow-up. Given the high number of false positives that can occur and the variability in technique and interpretation of the test in different laboratories, the presence of OCBs is not equivalent to a diagnosis of MS. Numerous other candidate biomarkers in serum and cerebrospinal fluid have been described, but none so far have the validated reliability necessary for widespread clinical use.(11) Given the abovementioned unmet needs, we explore in the current study a rapid, simple, and relatively inexpensive point-of-care test approach for the diagnosis of MS. The approach is based on the detection of volatile organic compounds (VOCs) that are emitted from cells and breakdown products of cells to the body fluids and ultimately appear in the breath.(12-14) Breath testing has been recognized as a medical technique that allows diagnosis of diseases by linking specific VOCs metabolites in exhaled breath to medical conditions (e.g., lung cancer, gastric cancer, and Parkinson’s).(15-32) In certain instances, breath analysis offers several potential advantages, such as: (a) breath samples are non-invasive and easy to obtain; (b) breath contains less complicated mixtures than either serum or urine; and (c) breath testing has the potential for direct and real-time monitoring.(12, 17, 33) Previously, we have performed a proof of concept study, for MS detection via breath analysis.(34) We showed that using a cross-reactive array of chemresistor sensors and a small cohort (51 subjects) we were able to discriminate between MS and control subjects from exhaled breath. We further identified two suspected VOCs using GC-MS analysis as potential biomarkers.(34) In this study, we expanded the study and examined 204 new subjects to further validate the reported findings and to


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evaluate the MS’s sub-populations, viz. MS in remission and MS in relapse; as well as sub-categories of treated MS and not-treated (NT) which include: naive MS never treated; and those currently without treatment for more than six months. A significantly increased study population (more than four times larger than the previous study) provides more robust statistical strength. We further examine a new generation of nanomaterial-based chemical sensors that have high potential to be affordable, cost-effective, and easy-to-operate when using exhaled breath samples. In this study, the data was evaluated both during training and blinded validation phases.



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RESULTS In the current study, two breath samples were collected from 204 volunteers: 58 controls and 146 MS patients representing different sub-population. Briefly, 146 MS patients from two sub groups: I) remission (128) and II) relapse (18). Same population can be further categorize into yet two additional groups: I) treated, DMT (112) II) not treated (34). The latter not-treated (NT) group include: naive MS never treated; and those without treatment for more than six months, prior to sampling, that is, stopped all medication at least six month before sampling. The adjuncting of the latter group to the naïve group was done to enable better statistical analysis power to the "No Treatment group". Comparison between treated and non-treated MS participants provides us with an answer on the effect of medication on the breath print if any. Demographic and clinical data of the tested population is summarized in Table 1. Primary confounding factors (e.g. Age, Smoking habits, geographic location, etc.) were controlled and evaluated. All samples were collected at the same location at the Multiple Sclerosis Center, Carmel Medical Center, Haifa, Israel. Confounding factors may interfere with the classification of interest (i.e. MS diagnosis) and were therefore evaluated.

Table 1: Demographic and clinical data of the study's population Demographic data Number of participants Age (mean ± SD) Gender F/M (ratio) Smoking Yes/No Past smoker Clinical data of MS patients Disease status sub-populations (%) EDSS(a), Median (range) Treated Disease Modifying Therapies

MS patients 146 39.5 ± 11.1 96/50 (1.95) 40/98 8

Control 58 39.7 ± 11.1 38/20 (1.90) 12/40 6

Remission Relapse Total

128 (88) 18 (12) 146 3 (0 – 7.5) 65 (58) 16(15)

Interferon Beta Copaxone


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(DMT) (%)

Not Treated (MS-NT) (%)

(a) (b) (c)

Gilenya Tysabri Sub-Total Naïve(b) No treatment(c) Sub-Total

13 (11) 18 (16) 112 22 (65) 12 (35) 34

EDSS – Expanded Disease Severity Scale Never treated At least 6 months without treatment

The collected samples were evaluated using both gas-chromatography linked with mass-spectrometry (GC-MS) and nanomaterial-based sensors (termed, NA-NOSE) (Figure 1). GC-MS is used for selective chemical VOC analysis for potential biomarker identification and pathway analysis. Sensors array combined with pattern recognition algorithm provide a fast ‘breath-print’ analysis for simple binary classification of the sample. Briefly, each sensor is composed either from gold nanoparticles (GNPs) or single-wall carbon nanotubes (SWCNTs) covered with diverse organic films (ligands). VOCs are diffused into the organic film layer which serves as a receptor, and the GNP\SWCNT (inorganic nanomaterials) serve as a transducer generating electrical conductivity.



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Figure 1: Study design. Breath from 204 people were collected and chemically analyzed by means of GC-MS for biomarker detection. In parallel, samples were exposed to nanomaterialbased sensors array for determining a breath pattern for disease.

GC-MS chemical analysis Using GC-MS analysis, approximately 160 VOCs were identified in the breath samples of the subjects after discarding possible contaminates and system bleeding compounds such as siloxane related VOCs. Five main VOCs showed significant differences with a cut-off p-value = 0.01 between all MS patients and the control group. Out of the five VOCs, acetophenone was found in higher levels in the control -8ACS Paragon Plus Environment

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group. On the other hand, heptadecane, nonanal, decanal, and sulfur dioxide were found in higher levels in the MS group (Table 2 and Figure 2).

Table 2: The main tentatively identified VOCs that showed significant differences between all MS patients in comparison to controls. VOC

CAS#

Formula

p-Value

Acetophenone

98-86-2

C8H8O

0.0004

Heptadecane

629-78-7

C17H36

Exhaled Breath Markers for Non-Imaging and Non ... - ACS Publications

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