WO2017042418A1 - Biomarkers in exhaled air for the diagnosis, classification and monitoring of lung cancer - Google Patents

Abstract

The invention relates to a method for obtaining data that can be used for the diagnosis, classification and monitoring of lung cancer, to a kit or device, and to the use thereof.

Description

 Biomarkers in exhaled air for the diagnosis, classification and monitoring of lung cancer

FIELD OF THE INVENTION The present invention is within the field of biomedicine and biotechnology, and specifically refers to a method of obtaining useful data for the diagnosis, classification and monitoring of cancer, preferably lung cancer, by analyzing Biomarkers: p-cresol, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-f-butylphenol, monosterate, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin- 1-ol, methyl stearate, glycerol-1-palmitate, benzyl alcohol, 2,4-diphenyl-4-methyl-2- E-pentane in an isolated biological sample, preferably condensate of exhaled air.

BACKGROUND OF THE INVENTION

Chronic respiratory disease is a generic term that encompasses long-term pathological conditions that affect the airways and other lung structures involved in breathing. Among these chronic diseases, two pathologies can be highlighted: chronic obstructive pulmonary disease (COPD) and lung cancer, the latter belonging to the list of the main causes of death related to the disease in the more developed countries [1,2]. The high mortality rate due to the most serious respiratory diseases is associated with the low efficiency of screening methods for their detection in early stages [3]. The search for diagnostic tests for the early detection of chronic respiratory diseases has intensified in recent decades. Low-dose computed tomography (CT) is currently the most widely used test for lung cancer, as it reduces the mortality of the high-risk population by about 20% compared to chest radiography, according to the National Study of Lung Exams [4]. CT has also proven useful for the evaluation of COPD [5].

In addition to tests based on imaging techniques, different studies based on the "omic" disciplines have focused on the development of assessment tools to diagnose respiratory diseases, mainly looking for potential biomarkers either in tissues or in biofluids. The methods that use samples of biofluids obtained in a non-invasive way are gaining popularity against invasive sampling. This is the case of urine or sputum and, more recently, exhaled breath or sweat [6]. A recent study on sweat that discriminates patterns of Metabolites for lung cancer screening [7], has resulted in an optimal panel of 5 compounds that provide 80% specificity and 79% sensitivity and lead to false positive and negative rates around 20% [8 ]. As for exhaled breathing, nitrite levels in the epithelial fluid have demonstrated their ability as a biomarker since an increase in this anion is directly associated with cancer [9]. Individual cytokines have also been investigated in the exhaled breath of patients with lung cancer using enzymatic immunoassays (EIA), but commercially available evidence with the required sensitivity for the detection of these compounds is lacking [10-13]. Other biomarkers (for example, hydrogen peroxide [14], 8-isoprostane [14] or the pH value [15]) allow discrimination between patients with COPD and healthy controls; however, they are not able to discriminate between these two groups of individuals and patients with lung cancer [16]. Phillips et al., Focusing on exhaled air vapor, have developed a mathematical model consisting of 22 compounds (mainly alkanes and their derivatives and benzene derivatives) as biomarkers of primary lung cancer. The model reported sensitivity and selectivity values of 71.7% and 66.7%, respectively, even for the advanced stages of the disease [17]. This study was subsequently improved by including in the model a set of oxidative stress products excreted in the breath [18], and a sensitivity and specificity of 85, 1% and 80.5%, respectively, were achieved. Later, another study concluded that a two-minute breath test can predict lung cancer regardless of histology, disease stage or tobacco use, with 84.6% sensitivity and 80% specificity [19] . A recent study has provided a panel of 23 volatile organic compounds in exhaled breath with the ability to distinguish between patients and lung cancer controls with 96.5% sensitivity and 97.5% specificity [20]. However, the criteria for the classification of endogenous compounds was not restrictive and some of the compounds included in the panel were also detected in the air of the sampling room.

In addition to the direct analysis of exhaled air using the appropriate interface, the sample to be analyzed may be condensed exhaled air (EBC); that is to say, the exhaled gas that condenses as a liquid solution by cooling, whose analysis can allow to know approximately the composition of the extracellular liquid and soluble exhaled gases. Although the main component of EBC is water vapor, it has hundreds of different components, it can be found from small inorganic ions through large organic molecules to peptides, proteins, surfactants and macromolecules [23] in trace concentrations [24-27 ]. As for the sampling protocol for the EBC, there are commercial devices that allow collection of two EBC fractions by separating the air from the upper and central airways (upper breath, UA) and the distal airway (lower breath, DA) . Although it is believed that the UA contains compounds without clinical relevance [23], no differences between both types of samples have been clearly established. The fractionation of the EBC allows the comparison of the composition profiles of the UA and DA to find metabolomic differences in patients with lung cancer compared with a group of risk factor composed of patients with COPD and with active smokers. A third group was used as a control group, which was formed by healthy non-smoking individuals. The UA and DA composition profiles for the three groups were obtained using gas chromatography coupled to high resolution mode mass spectrometry (GC-TOF / MS).

BRIEF DESCRIPTION OF THE INVENTION

A first aspect of the invention relates to the use of p-cresol metabolites, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-dis-dimethylbenzyl-6-t-butylphenol, monostetin, spiro-2, 4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1-palmitate, benzyl alcohol and 2,4-diphenyl-4-methyl-2-E -pentane for the diagnosis, classification and monitoring of cancer.

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer. A second aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer, henceforth the first method of the invention, comprising: a) quantifying the p-cresol metabolites, cumyl alcohol, eicosenamide and hexadecylindane in a biological sample isolated from said individual. In a preferred embodiment of this aspect of the invention, the first method of the invention further comprises: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals. In another preferred embodiment of this aspect of the invention, the cancer is lung cancer. In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air.

In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (a) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE).

A third aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereinafter second method of the invention, comprising steps (a) - (b) of the first method of the invention, and furthermore it comprises: c) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites are detected, preferably of the 4 metabolites of step (a), in amounts significantly different from the reference amount .

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer.

A fourth aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer, henceforth the third method of the invention, comprising: a) quantifying the 2,4-bis-dimethylbenzyl-6-t-butylphenol metabolites, monostetin, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol and methyl stearate in a sample biological isolated from said individual.

In a preferred embodiment of this aspect of the invention, the third method of the invention further comprises: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in individuals with at least one risk factor for cancer, preferably smoking or with COPD. In another preferred embodiment of this aspect of the invention, the cancer is lung cancer.

In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air. In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (a) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE).

A fifth aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereafter referred to as the fourth method of the invention, comprising steps (a) - (b) of the third method of the invention, and also it comprises: c) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites, preferably of 4 metabolites, and more preferably of 5 metabolites of step (a) are detected, in amounts significantly Different reference quantity.

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer.

A sixth aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer, hereafter referred to as the fifth method of the invention, which comprises: a) quantifying the Metabolites glycerol-1-palmitate, benzyl alcohol, monostetin, 2,4-diphenyl-4-methyl-2-E-pentane and p-cresol in a biological sample isolated from said individual.

In a preferred embodiment of this aspect of the invention, the fifth method of the invention further comprises: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals.

In another preferred embodiment of this aspect of the invention, the cancer is lung cancer.

In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air.

In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (a) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE). A seventh aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereafter sixth method of the invention, comprising steps (a) - (b) of the fifth method of the invention, and furthermore It comprises: c) including the individual from step a) in the group of individuals at high risk for cancer, when levels of at least 2 metabolites are detected, preferably 3 metabolites, more preferably 4 metabolites, and even more preferably 5 metabolites of step (a), in significantly different amounts of reference amount.

An eighth aspect of the invention relates to a kit or device comprising the elements necessary to quantify p-cresol metabolites, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-t-butylphenol , monostearin, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1 palmitate, benzyl alcohol and / or 2, 4-diphenyl-4-methyl-2-E-pentane, as described in any of the methods of the invention.

A ninth aspect of the invention relates to the use of the kit or device according to the preceding claim, for the diagnosis, classification and monitoring of cancer.

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1. Bar chart comparing the composition of EBC samples collected from the upper and central airways (UA) and the distal airway (DA).

Figure 2. Discriminant analysis by partial least squares (PLS-DA) performed from the data set obtained after the analysis of expired condensed air extracts from upper and central airways comparing patients (A) with lung cancer (LC) and people with risk factor (FR), (B) patients with lung cancer and control (control) individuals, and (C) individuals with risk factor and control (control) individuals.

Figure 3. Discriminant partial least squares analysis (PLS-DA) performed from the data set obtained after the analysis of expired condensed air extracts from the distal airway comparing patients (A) with lung cancer (LC) and people with risk factor (FR), (B) patients with lung cancer and control (control) individuals, and (C) individuals with risk factor and control (control) individuals. Figure 4. Box and whisker diagrams showing the variability of (A) four metabolites - monopalmitin, n-hexadecyldane, monostearin and squalene - in patients with lung cancer (LC) compared to individuals with risk factor (FR) and the cohort of control individuals, and (B) four metabolites— 1 1-eicosenamide, p-cresol, indole, benzoic acid 4-ethoxy ethyl ester in control individuals compared to lung cancer patients and individuals with risk factor.

Figure 5. Calibration curve of the retention index provided by the standard mixture of alkanes using the chromatographic method used.

Figure 6. Principal Component Analysis (PCA) constructed from the data obtained with the expired condensed air extracts of the upper and central airways and the distal airway of patients with lung cancer (LC), people with risk factor (FR) and control individuals.

Figure 7. Principal Component Analysis (PCA) constructed from the data set obtained through the analysis of expired condensed air extracts from the upper and central airways comparing patients (A) of lung cancer (LC) and people with risk factor (FR), (B) of lung cancer patients and control individuals (Control), and control individuals (C) and individuals with risk factor.

Figure 8. Principal Component Analysis (PCA) constructed from the data set obtained after the analysis of expired condensed air extracts from the distal airway comparing patients (A) of lung cancer (LC) and people with factor of risk (FR), (B) of lung cancer patients and control (control), and control (C) individuals and individuals with particular risk factor.

DETAILED DESCRIPTION OF THE INVENTION The authors of the present invention have identified and calculated the relative concentration of the different exhaled air condensate metabolites in individuals suffering from lung cancer, in individuals who present some risk factor of suffering it, and in healthy individuals. . They have found a series of markers for the diagnosis of individuals with lung cancer, differentiating subjects with lung cancer from those who do not. Thus, the present invention provides a method of obtaining useful data for the diagnosis, classification and monitoring of individuals with lung cancer. Therefore, a first aspect of the invention relates to the use of p-cresol metabolites, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-t-butylphenol, monostetin, spiro -2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1-palmitate, benzyl alcohol and 2,4-diphenyl-4-methyl- 2-E-pentane for the diagnosis, classification and monitoring of cancer.

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer.

Although any metabolite, or any combination thereof, can be used, more preferably the metabolites are used grouped as follows.

Group I) p-cresol, cumyl alcohol, eicosenamide and / or hexadecyldane,

 Group II) 2,4-bis-dimethylbenzyl-6-t-butylphenol, monosterate, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol and / or methyl stearate, and

Group III) glycerol-1-palmitate, benzyl alcohol, monosterate, 2,4-diphenyl-4-methyl-2- E-pentane and / or p-cresol, using at least one group. In a preferred embodiment of this aspect of the invention the cancer is lung cancer.

The characteristics of the metabolites that are the object of the study are described below: p-Cresol: a phenolic compound of low molecular weight known, among other things, as a degradation product of toluene and which, therefore, can be directly related to the tobacco use In addition, it is a metabolite of the amino acid tyrosine and to some extent phenylalanine, due to the conversion to 4-hydroxyphenylacetic acid caused by intestinal bacteria before being decarboxylated to p-cresol (putrefaction).

Cumyl alcohol: Monoterpene proposed as a marker for the detection of lung cancer in other studies that use exhaled air (vapor) as biofluid.

Eicosenamide: A fatty acid derivative known for its potential as an antimicrobial agent.

Hexadecylindane: Derived from the indanes proposed as a marker for the detection of lung cancer in other studies using exhaled air (vapor) as biofluid. 2,4-Bis-dimethylbenzyl-6-t-butylphenol: Compound derived from benzene related to tobacco consumption.

Monostearin: It belongs to the family of monoacrylic glycerols, glycerides that consist of a fatty acid chain covalently linked to a glycerol molecule through an ester bond.

Espiro-2,4-heptane-1, 5-6-methylene: There is no information about its existence in exhaled air or in any other biofluid.

13-Heptadecin-1-ol: There is no information about its existence in exhaled air or in any other biofluid. Methyl stearate: A fatty acid derivative previously identified in feces.

Glycerol-1-palmitate: It belongs to the family of monoacrylic glycerols, glycerides that consist of a fatty acid chain covalently linked to a glycerol molecule through an ester bond.

Benzyl alcohol: Benzene derivative known to be a product of the degradation of toluene and, therefore, can be directly related to tobacco consumption.

2,4-Diphenyl-4-methyl-2-E-pentane: Compound derived from benzene related to tobacco consumption.

A second aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of cancer, hereinafter the first method of the invention, comprising: a) obtaining an isolated biological sample from an individual, b) quantify the metabolites p-cresol, cumyl alcohol, eicosenamide and hexadecylindane in the sample from step a).

In a preferred embodiment of this aspect of the invention, the first method of the invention further comprises: c) comparing the amounts obtained in step (b) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals. In another preferred embodiment of this aspect of the invention, the cancer is lung cancer.

In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air. In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (b) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE).

A third aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereinafter second method of the invention, comprising steps (a) - (c) of the first method of the invention, and furthermore it comprises: d) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites are detected, preferably of the 4 metabolites of step (b), in amounts significantly different from the reference amount .

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer. A fourth aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer, henceforth a third method of the invention, comprising: a) obtaining a biological sample isolated from an individual, b) quantify the metabolites 2,4-bis-dimethylbenzyl-6-t-butylphenol, monostetin, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin -1-ol and methyl stearate in the sample from step a).

In a preferred embodiment of this aspect of the invention, the third method of the invention further comprises: c) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in individuals with at least one risk factor for cancer, preferably smoking or with COPD.

In another preferred embodiment of this aspect of the invention, the cancer is lung cancer. In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air.

In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (b) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE).

A fifth aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereafter referred to as the fourth method of the invention, comprising steps (a) - (c) of the third method of the invention, and also it comprises: d) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites, preferably of 4 metabolites, and more preferably of 5 metabolites of step (b) are detected, in amounts significantly other than the reference quantity.

In another preferred embodiment of this aspect of the invention, the cancer is lung cancer. A sixth aspect of the invention relates to a method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer, hereinafter fifth method of the invention, comprising: a) obtaining a isolated biological sample an individual, b) quantify the metabolites glycerol-1-palmitate, benzyl alcohol, monostetin, 2,4-diphenyl-4-methyl-2-E-pentane and p-cresol in the sample from step a ).

In a preferred embodiment of this aspect of the invention, the fifth method of the invention further comprises: c) comparing the amounts obtained in step (b) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals.

In another preferred embodiment of this aspect of the invention, the cancer is lung cancer.

In another preferred embodiment of this aspect of the invention, the biological sample isolated from step (a) is exhaled air, preferably condensate of exhaled air. In another preferred embodiment of this aspect of the invention, the quantification of the metabolites of step (b) is performed by GC-TOF / MS analysis, preferably with electronic impact ionization (IE).

A seventh aspect of the invention relates to a method of diagnosis, classification and monitoring of cancer, hereafter referred to as the sixth method of the invention, comprising steps (a) - (c) of the fifth method of the invention, and also It comprises: c) including the individual from step a) in the group of individuals at high risk for cancer, when levels of at least 2 metabolites are detected, preferably 3 metabolites, more preferably 4 metabolites, and even more preferably 5 metabolites of step (b), in significantly different amounts of reference amount.

In another preferred embodiment of this aspect of the invention, the cancer is lung cancer.

A "biological sample", as defined herein, is a small part of a subject, representative of the whole and may be a biopsy or a sample of body fluid. Biopsies are small pieces of tissue and can be fresh, frozen or fixed, as fixed with formalin and embedded in paraffin (formalin-fixed and paraffin embedded FFPE). Samples of body fluids can be blood, plasma, serum, urine, sputum, cerebrospinal fluid, milk or ductal fluid samples and can also be fresh, frozen or fixed. Samples can be surgically removed, by extraction, that is, by hypodermic or other needles, by microdissection or laser capture. The sample must contain any suitable biological material to detect the desired biomarker or biomarkers / s, therefore, said sample could comprise material from the subject's cells. The sample (s) used to develop the methods of the invention are a gas sample or bronchoalveolar lavage. Preferably the sample is exhaled air, and more preferably condensate of exhaled air.

A "reference sample", as used herein, means a sample obtained from individuals, preferably two or more individuals, known to be disease free (cancer, preferably lung cancer) or, alternatively, of the general population. Suitable levels of metabolites can be determined by measuring the levels of said metabolites in several suitable individuals, and such reference levels can be adjusted for populations of specific individuals or subjects. In a preferred embodiment, the reference sample is obtained from a group of healthy individuals or subjects or of subjects without a previous history of lung cancer. The amount and / or concentration of the metabolites in the reference sample can preferably be generated from a population of two or more individuals; for example, the population may comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals or subjects. In another preferred embodiment the reference sample is obtained from a group of risk individuals, preferably smokers and / or COPD patients.

An "individual" or "subject", as used herein, refers to a mammal, human or non-human, under observation, and more preferably a human being. The individual can be anyone, an individual predisposed to a disease (for example, lung cancer) or an individual suffering from said disease.

The term "quantity", as used in the description, refers to, but is not limited to, the absolute or relative amount of metabolites, of their concentration in exhaled air, preferably in exhaled air condensate, as well. as to any other value or parameter related to them or that may be derived from them. The amount of metabolites can be measured directly or indirectly.

The term "comparison", as used in the description, refers to, but is not limited to, the comparison of the quantity and / or concentration of the metabolites of the biological sample to be analyzed, also called the biological problem sample, with a quantity and / or concentration of metabolites of one or several desirable reference samples. The reference sample can be analyzed, for example, simultaneously or consecutively, together with the problem biological sample. The comparison described in section (c) of the method of the present invention can be performed manually or assisted by a computer.

Suitable reference amounts can be determined by the method of the present invention from a reference sample that can be analyzed, for example, simultaneously or consecutively, together with the problem biological sample. Thus, for example but without limiting ourselves, the reference sample may be the negative controls, that is, the amounts detected by the methods of the invention in samples of individuals who do not suffer from the disease or in individuals with risk factor for suffering from the disease. (smoking and COPD). Steps (b) and / or (c) of the methods described above can be fully or partially automated. The metabolites that are determined in step (b) can be determined individually, or any of their combinations can be determined. The metabolites can be determined by any means known to the person skilled in the art. The quantification of the metabolites of step (b) of the methods of the invention is preferably performed by analysis by GC-TOF / MS, preferably with electronic impact ionization (IE). In this report, the analytical equipment used and designated by GC-TOF / MS, from the English Gas Chromatography-Time of Flight Mass Spectrometry, has allowed the development of a suitable method for the detection of organic molecules with a molecular weights of up to approximately 1000 Da. The GC-Q / TOF offers high sensitivity and selectivity with the added value of providing accurate and high resolution information for the structural confirmation of metabolites.

The determination of the amount and / or concentration of metabolites can be done, for example, but not limited to, by means of an indicator system prepared on a solid support (paper or solid sorbent) in which selective or specific reagents for the compounds have been immobilized. identified as markers. An eighth aspect of the invention relates to a kit or device comprising the elements necessary to quantify the metabolites p-cresol, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-t-butylphenol , monostetin, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1 palmitate, benzyl alcohol and / or 2 , 4-diphenyl-4-methyl-2-E-pentane, as described in any of the methods of the invention.

In another preferred embodiment of this aspect of the invention, the kit or device of the invention further comprises all those elements necessary to carry out an analysis by GC-TOF / MS.

The kit can also include, without any limitation, buffers, solutions for protein extraction, agents to prevent contamination, inhibitors of protein degradation, derivatizing reagents, etc.

In another preferred embodiment of this aspect of the invention, the kit or device of the invention is a kit of parts, comprising a component A, formed by a device for collecting the sample from step a), and a component B, formed by the elements necessary to carry out the qualitative, semi-quantitative or quantitative analysis in the sample of step a) or any of the methods of the invention On the other hand, the kit can include all the supports and containers necessary for its start-up and optimization. Preferably, the kit further comprises instructions for carrying out the methods of the invention.

A ninth aspect of the invention relates to the use of the kit or device according to the preceding claim, for the diagnosis, classification and monitoring of cancer.

In a preferred embodiment of this aspect of the invention, the cancer is lung cancer.

Throughout the description and the claims the word "comprises" and its variants are not intended to exclude other technical characteristics, additives, components or steps. For those skilled in the art, other objects, advantages and features of the invention will be derived partly from the description and partly from the practice of the invention. The following examples and drawings are provided by way of illustration, and are not intended to be limiting of the present invention.

EXAMPLE OF THE INVENTION Materials and methods

Reagents

TraceSELECT® hexane from Sigma-Aldrich (St. Louis, USA) was used as the organic solvent for sample preparation, a standard mixture of alkanes (from C10 to C40) also from Sigma-Aldrich for separation tests of GC and to establish the retention index calibration (Rl). Deionized water (18 mO · cm) of a Millipore Milli-Q water purification system was also used.

Instruments and devices

An ECOScreen2 device (FILT Thorax-und LungenDiagnostik GmbH, Berlin, Germany) was used to collect EBC. The homogenization of the extracts was carried out by an MS2 Minishaker Vortex (IKA, Germany).

An Agilent 7890A Series GC system coupled to an Agilent 7200 UHD Accurate-Mass QTOF hybrid mass spectrometer equipped with an electronic impact source (El) (Santa Clara, CA, USA) was used. The analytical sample was monitored in high resolution mode. Cohort selected for the study The EBC samples were obtained from 239 fasting individuals, and stored at -80 ° C in the bio-repository of the Reina Sofía Hospital (Córdoba, Spain). All individuals were recruited in the Department of Respiratory Medicine. The cohort included 48 patients diagnosed with lung cancer between November 2012 and May 2014. All patients were diagnosed with lung cancer and were confirmed cytohistologically after clinical trials based on bronchoscopy, fine needle biopsy or videothoracoscopy. . These patients had a mean of 63 ± 7 years and 94% of them were male. The most frequent diagnosis was squamous cell carcinoma (15 patients, 31, 25%), followed by adenocarcinoma (13 patients, 27%), small cell carcinoma (7 patients, 14.5%), large cell carcinoma ( 6 patients, 12.5%). Seven people (14%) were diagnosed with non-small cell lung cancer without histological classification.

The risk factor group consisted of 130 people, 83 of them active smokers (> 20 packages / year) and 47 individuals diagnosed with COPD using spirometry (FVC / FEV1 ratio <0.7). Two reasons supported the inclusion of smokers and patients with COPD in the risk factor group: first, smoking is considered the most important risk factor for developing lung cancer and COPD; second, the increase in oxidative stress or the presence of inflammatory cells infiltrated in COPD and in lung cancer are common forms of theoretical explanation of lung damage (especially the latter). The risk factor group, clinical control for at least one year, was characterized by an average age of 61 ± 8 years, with 82.4% of male individuals. The existence of lung cancer was ruled out in this group through CT and bronchoscopy. The control group consists of 61 healthy individuals with an average of 60 ± 9 years, 87% of them male individuals. All of them were non-active or passive smokers, without clinical symptoms and with a normal profile established by the chest radiograph.

The criteria for the exclusion of patients were: a) the coexistence of extrapulmonary tumor pathology or a treatment with cytostatic drugs for a different neoplasm; b) diagnosis of neoplasia in the last five years; c) unjustified weight loss (≥7 kg) during the past year; d) serious disorder of any organ with negative influence on the prognosis or that prevented the application of the protocol (in the cases it was included the degree IV of heart failure according to the New York Heart Association, advanced liver cirrhosis, renal failure in phase V with the substitute treatment with hemodialysis or peritoneal dialysis, and the diagnostic lung disease not related to smoking, including interstitial pneumopathy, pneumonia, tuberculosis, etc. [8]). All experiments were carried out in accordance with the ethical principles of medical research in humans (World Medical Association, Declaration of Helsinki). The ethics committee of the Reina del Hospital Sofía (Córdoba, Spain) approved and supervised the clinical study. EBC collection procedure

The ECOScreen2 device used for sampling allows direct collection and condensation of the EBC in disposable polyethylene bags. It is operated at -20 ° C, the collection of controlled EBC is carried out in two separate bags for the physical separation between the air contained within UA and that of DA [23]. The main modification was the insertion of a commercial protection filter from Scharlab (Barcelona, Spain) on the air inlet valve to prevent the entry of exogenous organic compounds and particles from the ambient atmosphere. This filter was changed periodically to avoid saturation.

The sampling of the breath (with a nose clip) was carried out for 15 minutes, time needed to collect an average volume of EBC of 1.5 ml of the DA and 1 ml of the UA. Only 101 people were able to provide a sufficient volume of the two fractions for analysis. The samples were divided into 100 μΙ aliquots and the vials were stored at -80 ° C until analysis. All samples were analyzed within 3 months after collection.

Sample preparation Sample preparation consisted of liquid-liquid extraction using hexane as an extractant. In all cases, 100 μΙ of aliquots of EBC and the same volume of hexane were vortexed in a glass insert at room temperature for 1 min. Then, the organic phase was isolated and placed in a new glass insert for analysis. To eliminate exogenous interference, the targets were prepared by using treated water as the samples.

GC-TOF / MS analysis

GC-TOF / MS analyzes were performed with electron impact ionization (El) at 70 eV and controlled by Masstiunter Acquisition B.06. Chromatographic separation was carried out on a fused silica capillary column DB-5MS-UI 30 m ^χ 0.25 mm id, 0.25 μηι thick. The temperature program for separation in the GC started at 60 ° C (1 min), followed by a temperature rise of 10 ° C / min to a final 300 ° C (2 min). A column temperature increase to 310 ° C was programmed for 4 min to ensure complete elution of all components of the injected sample. Injections of 1 μΙ of sample were carried out at 250 ° C without division of the flow, and ultrapure grade helium was used as carrier gas at a flow rate of 1.0 ml / min. The interface, ion source and quadrupole temperatures were set at 280, 300 and 200 ° C, respectively. The ion source filament was turned off until 5.5 minutes to prevent damage from the solvent front outlet. The TOF detector performed 5 spectra / s in the m / z range from 50 to 550 and the resolution was 8500 (width at half the maximum peak height, FWHM) at m / z 501, 9706. The high purity PFTBA (perfluorotri-n-butylamine) for mass spectrometry was used for daily mass axis calibration. The metabolites were identified by searching the MS spectra in the NIST 1 1 database taking into account the Rl values.

Data processing and statistical analysis

To process all the data obtained by GC-TOF / MS in the full analysis mode, the Unknown Analysis software (version 7.0, Agilent Technologies, Santa Clara, CA, USA) was used. The processing of raw data files was initiated by deconvolution of potential molecular entities (MFs) with the appropriate algorithm included in the software. For this purpose, the deconvolution algorithm for the absolute height parameter considered all ions greater than 1,500 counts. In addition, the precision error and window size were set at 50 ppm and 150, respectively. After the extraction of the MFs, the data files in compound file sharing format (.cef) were created for each sample and exported to the Mass Profiler Professional (MPP) software (version 12.1, Agilent Technologies, Santa Clara, CA, USA) for further processing.

In the next step, the data was processed by aligning the potential MFs according to their retention time and the m / z value using a 0.3 min tolerance window and an accuracy error of 15 ppm. The MFs from the analysis present in the targets treated as samples were removed from the final MFs data set for the EBC samples. The extraction algorithm confirmed the effectiveness of this filtering stage. This correction was applied to all the treatments of the analyzed samples. The gradual reduction of the number of MFs is based on the frequency of occurrence by comparing repetitions of the same group of people. A 100% frequency filter was set, thus ensuring the detection of each MF in all repetitions of each group of injected samples (lung cancer, risk factor group and control of healthy individuals). In the last step, the resulting MFs were exported (.cef file) for recursive analysis. For this purpose, Quantitative Analysis software (version 7.0, Agilent Technologies, Santa Clara, CA, USA) was used to reintegrate all potential compounds found in the analyzed samples. The resulting table is exported in comma separated values format (.csv file) and reprocessed with the Mass Profiler Professional (MPP) software package. A filter was applied to remove the samples with a replication within the variability above 10% to ensure the effectiveness of the recursive analysis. Finally, the data set was normalized by logarithmic transformation of the relationship between the peak area of each molecular entity and the total sum of the areas of the MFs present in 100% of the samples (MSTUS).

Next, the resulting resulting data from each EBC fraction were subjected to supervised and unsupervised analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). As a validation method cross-validation was selected through the use of a N-fold model. With this model, the classes in the input data are randomly divided into N equal parts; N-1 parts are used for training and the remaining part is used for the test. The process was repeated N times, with a different subset that is used to perform tests in an iterative process. Therefore, each row is used at least once in the training and once in the test, and a confusion matrix is generated. The entire process can be repeated as many times as specified by the number of repetitions. The validation in this investigation consisted of ten repetitions and a number of times of three.

Finally, a paired f-test was applied to compare the two fractions of the same patient's EBC, and an unpaired f-test was used to compare the groups under study using a Bonferroni-Holm correction test.

Identification of potential MFs detected by GC-TOF / MS

The identification was first carried out by searching for MS spectra in the NIST1 database 1. Only identifications with a coincidence factor and an inverse coincidence factor greater than 700 were considered valid. Rl values included in The NIST database was also taken into account to support the identifications. An Rl calibration model was constructed by comparing the Rl values of a standard mixture of alkanes (composed of C10 and C40 alkane between an even number of carbon atoms) using the chromatographic method proposed in this investigation and the values of Rl provided by the NIST database. Figure 1 Supplementary shows the Rl calibration line obtained by this approach. The requirement to accept the NIST identifications was that the difference between the theoretical and experimental Rl, obtained by extrapolation of the calibration curve, must be within ± 100 units. The NIST database does not contain high resolution MS information as provided by the TOF detector. For this reason, a third step was included to validate the identification of each MF through the use of high resolution mass spectrometry. Therefore, the molecular formula for the tentative precursor ion ([M] ⁺ ) and the most intense product ions obtained for each MF must fit the NIST identification by setting a mass cut-off value of 10 ppm. Table 1 contains the identified compounds classified by chemical families.

Table 1. Compounds in EBC extracts identified by GC-TOF / MS

 NAME OF THE TIME OF

FORMULA CAS ID Fragments Family COMPUEST RETENTION

 OR

154.1361 - [C10H18O] ⁺

 Compounds

Eucalyptol 5,995 CioHieO 470-82-6 139.1 1 19 - [C9H150] ⁺ aliphatic heteropolycyclics (oxanes) 93.0695 - [C7H9] ⁺

1 17.0558 - [C8H7N] ⁺ Heterocyclic compounds

Indole 9,904 C ₈ H ₇ N 120-72-9 90.0448 [C7H6] ⁺

aromatic 74.0145 - [C6H2] ⁺ (Índoles)

 194.0425 - Compounds

[C1 1 H1403] acid ⁺ benzoic aromatic 4-

12,923 C11 H14O3 23676-09-7 149.0581 - [C9H902] ⁺ homomonocyclic ethoxy-ethyl (benzene and ester 121 .0269 - [C7H502] ⁺ substituted derivatives)

Ester 136.0514 - [C8H802] ⁺ Methyl aromatic compounds of

6,951 C8H8O2 93-58-3 105.0332 - [C7H50] ⁺ homomonocyclic acid

(benzoic and benzoic acid 77.0378 - [C6H5] ⁺

 derivatives)

276.1712 -

3,5-di- [C17H2403] acid ⁺ t-Butyl-4- 261 .1479 - aromatic compounds

 20, 155 C17H24O3 22014-01 -3

hydroxycinnamic [C16H2103] ⁺ homomonocíclicos

 (cinnamic acid and

CO 177.0896 - derivatives) [C1 1 H1302] ⁺

290.1867 - Compounds [C18H2603] ⁺ aromatic

Octinoxate 21, 005 C18H26O3 5466-77-3 178.061 1 - homomonocíclicos

[C10H10O3] ⁺ (cinnamic acid and

161 .0578 - [C10H9O2] ⁺ derivatives)

136.0514 - [C8H802] ⁺ Compounds p-Cresol 6,536 C ₇ H ₈ 0 106-44-5

105.0332 - [C7H50] ⁺ aromatic homomonocylic

77.0378 - [C6H5] ⁺ (phenols and cresol derivatives)

108.0565 - [C7H80] ⁺ Aromatic compounds

Alcohol

5.963 C ₇ H ₈ 0 100-51-6 91.0535 - [C7H7] ⁺ benzyl homomonocyclics

 (alcohols

79.0533 - [C6H7] ⁺

 primary)

201.1835- [C12H2502] ⁺

 The time of Lipids (esters of

 14.97 C15H30O2 10233-13-3

Isopropyl 157.1203 - [C9H1702] ⁺ fatty acids)

102.0656 - [C5H10O2] ⁺

 270.2545 -

Ester [C17H3402] ⁺

 methyl

17,342 C17H34O2 112-39-0 227.1998- Lipids (esters of acid [C14H2702] ⁺ fatty acids) palmitic

143.1048 - [C8H1502] ⁺

 296.2702 -

Esther [C19H3602] ⁺

 Lipids (19,041 C19H36O2 methyl esters 112-62-9

264.2442 - [18C32HO] ⁺ fatty acids) oleic acid

81.0685 - [C6H9] ⁺

 298.2862 -

Ester [C19H3802] ⁺

 methyl

19.28 C19H38O2 112-61-8 255.2315- Lipids (esters of acid [C16H3102] ⁺ fatty acids) stearic

87.0436 - [C4H702] ⁺

236.2122 - [C16H280] ⁺

 Acid Lipids (esters of

17,452 C16H30O2 373-49-9 98.0710 - [C6H10O] ⁺

palmitoleic fatty acids)

69.0689 - [C5H9] ⁺

227.1997- [C14H2702] ⁺ Lipids (acids

Acid

17,697 C16H32O2 57-10-3 fatty and palmitic 129.0891 - [C7H1302] ⁺

conjugates) 73.0279 - [C3H502] ⁺

284.2706 - [C18H3602] ⁺ Lipids (acids

Acid

19,584 C18H36O2 57-11-4 129.0908 - [C7H1302] ⁺ fatty and stearic

conjugates) 73.0281 - [C3H502] ⁺

297.2436 - [C18H3303] ⁺ Lipids (acids

Glycidol

 22,476 C21H40O3 7460-84-6

stearate 98.0719 - [C6H10O] ⁺ fatty and conjugated) 71.0848 - [C5H11] ⁺

111.1157 - [C8H15] ⁺

 Lipids (alcohols

Undecanol 1,125,125 C11H24O 112-42-5 83.0844 - [C6H11] ⁺

fatty) 69.0691 - [C5H9] ⁺

111.1160 - [C8H15] ⁺

 one-

Lipids (alcohols

Hexadecanol 15,994 C17H36O 2490-48-4 97.1006 - [C7H13] ⁺

 fatty) 2-methyl

69.0691 - [C5H9] ⁺

 281.2679-

Oleamide 21,328 C18H35NO 301-02-0 [C18H35NO] ⁺ Lipids (fatty amides)

126.0914 - [C7H12NO] ⁺

93.0695 - [C7H9] ⁺ monoterpenes)

410.3907 - [C30H50] ⁺

 Lipids (prenol

Squalane 24,835 C30H50 111-02-4 121.0994 - [C9H13] ⁺

lipids-triterpenes) 81.0686 - [C6H9] ⁺

368.3437 - [C27H44] ⁺

 Lipids (steroids

Cholestadiene 25,554 C27H44 747-90-0 247.2412 - [C18H31] ⁺ and steroid derivatives) 147.1141 - [C11H15] ⁺

203.0913 - [C9H1505] ⁺ Organic acids and

C ¡derivative treatment (acids

14.404 C12H20O7 77-93-0 157.0496 - [C7H904] ⁺

 triethyl carboxylic and

83.0486 - [C5H70] ⁺ derivatives)

136.1225 - [C10H16] ⁺

 Spiro [2,4]

heptane-1, 5- 121.0990 - [C9H3] ⁺ Other compounds

 5,953 C10H16 62238-24-8

dimethyl-6- organic methylene 79.0524 - [C6H7] ⁺

59.0485 - [C3H70] ⁺

 2-propanol

Other compounds 1- (2-butoxy- 9.21 C10H22O3 29911-28-2 86.0715 - [C5H10O] ⁺

 organic 1-methyl-ethoxy)

103.0728 - [C5H1102] ⁺

220.1822 - [C15H240] ⁺

 2,4,6-

Other Triisopropyl compounds 12,685 C17H26O 07/08/1934 205.1584 - [C14H210] ⁺

 organic nol

77.0369 - [C6H5] ⁺

 225.1826-

1,3- [C14H2502] ⁺

Other compounds Heptadecyn- 15.11 C17H32O 56554-77-9 81.0681 - [C6H9] ⁺ organic 1-ol

67.0529 - [C5H7] ⁺

236.1567 - [C18H20] ⁺

 2,4-diphenyl-4-

Other compounds methyl-2 (E) 16.45 C18H20 22768-22-5 143.0809- [C11H11] ⁺

 organic pentene

91.0513 - [C7H7] ⁺

7,9-Di-t-butyl- 175.1104- [C12H15O] ⁺

 oxapiro

Other compounds (4,5) deca- 17,182 C17H26O2 82304-66-3 133.0638 - [C9H90] ⁺

organic 6,9-diene- 2,8-dione 77.0369 - [C6H5] ⁺

10,18-242.2007 - [C18H26] ⁺

Bisnorabieta Other compounds

17,999,014 C18H26 32624-67-2 227.1790 - [C17H23] ⁺

-8,11,13- organic triene 143.0864 - [C11H110] ⁺

2,6-Di-t-butyl- 324.2438 - [C23H320] ⁺

 4- (2- Other compounds

18,709 C23H32O 34624-81-2 309.2212 - [C22H290] ⁺ phenylpropan- organic 2-yl) phenol 119.0836 - [C9H11] ⁺

Phenol 2,2'- 330.1984 - [C24H260] ⁺

 methylene-bis

 Other compounds

[6- (1,1-21,768 C23H32O2 119-47-1 315.1748 - [C23H230] ⁺

organic dimethyl) -4- methyl 237.1263 - [C17H170] ⁺

Phenol 2,4-bis 330.1984 - [C24H260] ⁺ Other compounds

 22,522 C24H26O 2772-45-4

(1-methyl-1- 315.1748 - [C23H230] ⁺ organic phenylethyl) - 237.1263 - [C17H170] ⁺

2,4-Bis 386.2617 - [C28H340] ⁺

 (dimethylbenzyl Other compounds

22.57 C28H34O 244080-16-8 371 .2370 - [C27H310] ⁺

) -6-t- organic butylphenol 293.1897 - [C21 H250] ⁺

1 17.0351 - [C9H9] ⁺

 N-

Other compounds hexadecilind 23,928 C25H42 55334-29-7 130.0427 - [C10H10] ⁺

 organic anus

154.1345 - [C1 1 H22] ⁺

Results

Comparison between UA and DA samples

The sampling device contains valves that allow separation according to the expiration route, in addition to the fractionation of the exhaled volume according to a volume threshold in the two cavities. This configuration makes saliva contamination highly unlikely [28]. Although EBC fractionation is supposed to separate exogenous and endogenous components, there are no previous studies that have evaluated the composition of the two EBC fractions. All the compounds identified were present in both UA and DA samples, but some of them showed differences between the fractions in terms of their relative concentration. Figure 1 shows the bar chart comparing the average relative concentration for the 47 compounds detected in the samples. As can be seen, most of the identified compounds were found in DA at higher concentrations than in the UA. A paired t-test revealed that 18 of these compounds had significantly different concentrations in the fractions in the samples collected in the two compartments (p value <0.05), which means that almost 38% of the EBC components identified they presented different concentrations in UA and DA (Figure 1). Nine compounds (among them the esters of fatty acids and squalene) were more concentrated in DA, while others such as elrereol, limonene, benzoic acid or 1,1-eicosenamide were more concentrated in the UA samples. Since the two EBC fractions were quantitatively different in composition, a multivariate statistical analysis of each fraction was carried out independently.

Multivariate analysis of each airway fraction An unsupervised analysis was applied to find differences in EBC collected in the three groups of samples for the UA and DA fractions. Principal component analysis was carried out with the data set that included 44 of the 47 compounds identified. Three compounds were excluded since their origin could only be explained by external sources such as cosmetics (slaveol and octinoxate) or plastic bags used to collect EBC (7,9-di-t-butyl-1-oxapiro (4 , 5) deca -6,9-diene-2,8-dione). Due to the high variability associated with the individuals, the three groups studied (lung cancer, group with risk factor and group of healthy individuals) appeared superimposed on the three dimensions of the PCA score chart for UA and DA (Supplementary Fig. 2) . For this reason, the unsupervised analysis was divided into three studies by including only two groups: lung cancer compared to a risk factor group, lung cancer compared to healthy control individuals, and the group with risk factor compared to healthy control individuals. Supplementary figures 3 and 4 illustrate the 3D graphs of the scores for the three cases using the UA and DA, respectively. In all cases, discrimination trends were observed between the two groups. Both EBC fractions showed a similar separation trend; however, intra-individual and inter-individual sources of variability do not allow complete separation of the groups evaluated. In the different PCA tests, the combination of PC1 / PC2 / PC3 does not explain the variability above 50% of the total variability contained in the study.

Supervised analysis was applied to find patterns of discrimination associated with the diagnosis of lung cancer. As in the previous study, the analysis was divided into three studies: lung cancer compared to risk factor group, lung cancer compared to healthy control individuals, and the risk factor group compared to individuals. of control. Figures 2 and 3 illustrate the scores on 3D graphics of the three independent studies for each fraction of EBC. As can be seen, the percentage of variability explained did not exceed 50%, but in this case the discrimination trends were clearly observed in all cases. The percentage of correctly classified samples for the joint validation and formation of the PLS-DA result for UA and DA is shown in Table 2. Patients with lung cancer are accurately classified in the combined model with healthy individuals, especially in the fraction of the AU that reported a recognition capacity of 97.0% in the training set and 88.2% in the validation stage. The recognition capacity decreased when the risk factor group was included in the classification analysis, which is quite logical since it is the intermediate group. Thus, in the AU the recognition capacity for the separation of this group from both lung cancer patients and healthy individuals was similar: 76.5% for the training set and 60.5% for the validation set. These values increased when the DA fraction It was the objective sample. The recognition capacity was also similar in the DA fraction, with 83 and 84% in the training set used for the separation of the risk factor group of lung cancer patients and healthy individuals, respectively, and 73 and 77.5% for validation sets. From a clinical point of view, the most interesting model to help in the diagnosis of lung cancer is the comparison between patients with lung cancer and the risk factor group. According to the parameter with recognition capacity, the DA fraction was characterized by a higher classification capacity than the UA fraction. The model provided by the DA analysis is well balanced in terms of sensitivity and specificity, these parameters being 89.8 and 79.5%, respectively. For this reason, the DA fraction was selected for other studies that deal with the identification of important compounds that help explain the observed patterns.

Table 2. Sampling frequencies classified for validation, training and recognition capacity for the development of PLS-DA models provided by the upper respiratory tract / center (UA) and the distal airway (DA) for the comparison of the three groups with each other (controls - Control—, controls with risk factor - RF— and patients with lung cancer— LC—)

UA DA airway

 RF vs Control Control CRF vs Control Control Models

 LC vs LC vs RF LC vs LC vs RF

Training model

Sensitivity (%) 92,857 92,857 67,532 89,796 89,796

Specificity (%) 67,532 97,057 97,059 79,542 94,000 90,000

Capacity of

 76,471 94,737 76,577 83,212 91, 919 84,783 recognition (%)

 Validation model

Sensitivity (%) 73,810 76, 190 55.844 83.673 77.551 75,000

Specificity (%) 53,810 88,235 73,529 67,045 82,000 82,000 Capacity

^H. . . _{/ o /} 60,504 71, 579 61, 261 72,993 79,798 77,536 recognition (%) Identification of significant compounds that contribute to explain clinical variability

As mentioned before, the compounds identified in the EBC of the three groups studied and used to construct the prediction models are shown in Table 1. An unpaired t-test was applied to identify the most important compounds that contributed to explain the differences observed between the three groups (table 3). Four compounds were significant in the comparison of patients with lung cancer compared to the risk factor group and that they formed by healthy individuals. Among these compounds, it is worth noting the presence of two saturated monoacrylglycerols (monopalmitin and monostearin), and an acyclic triterpenoid (squalene), which is the precursor of sterols, including cholesterol, and bile acids [29]. The presence of squalene in exhaled breath has been widely described [30]. This compound is structurally similar to isoprene, which is considered one of the most concentrated compounds in human respiration [22]. In fact, some authors have proposed that polyisoprenes such as squalene are considered potential sources of peroxidation isoprene, one of the mechanisms of oxidative stress [30]. However, isoprene was not detected in EBC in this investigation, which could be explained by its high volatility. The differences in the relative concentration of the two monoacrylic glycerols and squalene in the three groups studied can be seen in Figure 4. Monopalmitin and monostearin were characterized by different behaviors: monopalmitin was more concentrated in the risk factor group than in the patients with lung cancer, which also had a higher concentration of this monoacrylic glycerol than healthy individuals. On the other hand, the monostearin presented the opposite profile: as the risk factor group gave a lower relative concentration than patients with lung cancer. Squalane gave a concentration profile similar to that of monopalmitin. The fourth compound (hexadecyldane, a derivative of indane) has not been related to any endogenous source and, therefore, could be attributed to an exogenous origin. However, Phillips et al. [31] have also detected indane derivatives in respiration, and even selected one of them as a biomarker of lung cancer [32].

Table 3. Analysis of unpaired t-test using a Bonferroni- Holm multiple correction test to assess the importance of the metabolites identified in exhaled air condensate to discriminate between lung cancer patients, individuals with risk factors and Control individuals

LUNG CANCER VS COMPOSITE CONTROL P REGULATION FC

p-Cresol 9.00E-04 LOW Indole 3.51 E-04 LOW -2,77727

 Benzoic acid 4-ethoxy-ethyl ester 0.003489 LOW -1, 58336

 Triethyl Citrate 0.009728 HIGH 2,233156

 Monopalmitin 0,015986 HIGH 2,205814

 11 -Eicosenamide 3.13E-05 HIGH 2.16227

 n- Hexadecylindane 0.002707 HIGH 1, 952551

 Monostearin 0.00391 1 HIGH 2.608156

 LUNG CANCER VS CONTROL WITH RISK FACTOR

COMPOUND P REGULATION FC

 Hedione 0,041098 HIGH 1, 234381

 13-Heptadecin-1 -ol 0.002017 HIGH 1, 4398

 Monopalmitin 0.003899 HIGH 2,630078

 n- Hexadecylindane 4.81 E-04 HIGH 1, 815379

 Monostearin 1.07E-04 HIGH 3,018592

 Squalene 2.12E-04 HIGH 2.431075

 CONTROL VS CONTROL WITH RISK FACTOR

COMPOUND P REGULATION FC

 p-C resol 0.009083 LOW -2.27088

 Indole 0.004347 LOW -2.0566

 Undecanol 0,043269 LOW -1, 41217

 Benzoic acid 4-ethoxy-ethyl ester 0.004672 LOW -1, 35418

 Triethyl Citrate 0.027726 HIGH 1, 854397

 Hediona 0,01682 HIGH 1, 223202

 Monostearin 1.36E-07 HIGH 5.801463

 c11 -Eicosenamide 8.68E-05 HIGH 1, 799589

 n- Hexadecylindane 3.75E-12 HIGH 3,544619

 Monostearin 1.74E-12 HIGH 7.87296

 13-Docosenamide 5.19E-04 HIGH 1, 485012

Taking the group of healthy individuals as a reference, six compounds were significant in comparison with patients with lung cancer and the group with risk factor. Among them, triethyl citrate, which has been found in plastics and cigarette filters, detected at a higher concentration in patients with lung cancer and in individuals with risk factors than in healthy individuals, [33]; therefore, its presence in exhaled breath could be linked only to exogenous sources. A phenolic compound (p-cresol) and a phenol derivative (4-ethoxy ethyl benzoic acid ester) were also found at different concentrations in all three groups. P-cresol was detected at a lower concentration in patients with lung cancer than in individuals with risk factors, who also reported lower levels than healthy individuals. The benzoic acid ester derivative was found in lower concentrations in the lung cancer group and individuals with a risk factor than in the healthy group. Philipps et to the. They have identified this compound in human respiration as a potential candidate marker to discriminate lung cancer patients [32].

Another interesting compound was indole, which is involved in tryptophan metabolism, particularly in bacteria [34]. In addition, indole has been found in cigarette smoke and, in this context, could be associated with tobacco use [35]. The relative concentration profile of this compound is characterized by a higher concentration in the risk factor group followed by patients with lung cancer and then at a lower level in healthy individuals. Finally, other compounds that contribute to differentiate healthy individuals were eicosenamide and erucamide, present at lower levels in this group compared to individuals in the risk factor and lung cancer group. These fatty amides had not been described endogenously, but some analogue such as oleamide, which was also found in EBC, is structurally related to the endogenous cannabinoid anandamide, implicated in many biological functions. Fatty amides are also used as plastic additives and, for this reason, a source of contamination could also explain the presence of eicosenamide and erucamide in the EBC.

Three other compounds that were significant in the comparison of the risk factor group and the other two groups were two fatty alcohols (13-heptadecyn-1-ol and undecanol) and a derivative of jasmonic acid (hedione). Regarding 13 heptadecyn-1-ol, there is no previous information that describes it in the breath or about its biological implication in human processes. The other fatty acid (undecanol), along with 200 other compounds, have been previously detected in feces by Garner et al., Who studied the potential of undecanol for the diagnosis of gastrointestinal diseases [36]. The latter (hedione), which has been previously detected in saliva [37], could be a product of the oxidation of linoleic acid.

In summary, and as stated above, EBC has not been widely exploited in the clinical field despite the advantages associated with its sampling. A method of metabolomic analysis of EBC based on GC-TOF / MS profiles in high resolution mode has been developed using liquid-liquid extraction for sample preparation. The identified compounds have been used to discriminate between three different groups: individuals diagnosed with lung cancer, individuals with risk factor (including smokers and patients with COPD) and the control formed by healthy individuals. These compounds include the presence of monoacrylic glycerol derivatives of two of the four main saturated fatty acids and squalene, It could be considered as an intermediate product in the pathway for in vivo formation by the peroxidation of isoprene in human breath [39], and also involved in cholesterol synthesis [40]. These results support the potential of EBC as a biofluid to discriminate between lung cancer patients and the risk factor group, which could help in the diagnosis of this disease in the search for a screening method to reduce the use of a Confirmation test for the case of positive response of the former.

The results obtained could be summarized in the following table:

Table 4. Summary of markers useful in the diagnosis, classification and monitoring of lung cancer.

PATIENTS WITH CANCER OF PULMON VS. HEALTHY *

THRESHOLDS METABOLITE

 p-Cresol <0.0143707

Cumyl alcohol> 0.0172175

 Eicosenamide> 0.00450892

Hexadecylindane> 0.0268941

 PATIENTS WITH CANCER OF PL LMON VS. RISK FACTOR*

THRESHOLDS METABOLITE

 2,4-Bis-dimethylbenzyl-6-t-butylphenol> 0.0773647

 Monostearin <0.0666031

Spiro-2,4-heptane-1, 5-6-methylene> 210,452

 13-Heptadecyn-1-ol <0.00438287

 Methyl Stearate> 0.00245645

 RISK FACTOR PATIENTS VS. HEALTHY **

 THRESHOLDS METABOLITE

 Glycerol-1-palmitate> 0.0818449

 Benzyl Alcohol> 0.109225

Monostearin> 0.053136 2,4-Diphenyl-4-methyl-2-E-pentane <0.00557449

 p-Cresol <0.00680814

* (positive (cancer) when at least 3 metabolites meet the condition)

(positive (risk) when at least 2 metabolites meet the condition)

The values provided in this table are expressed as both by one with respect to all the metabolites detected in the sample (44 metabolites). That is, the area of each of the compounds was divided by the sum of the area of the 44 metabolites, thus obtaining the contribution of each compound to the total metabolites detected. On these final values the panels have been obtained, so that the results can be compared with those obtained using other instrumentation.

As for the operation of the panels themselves, they are configured so that, although they are composed of 4-5 metabolites, it is only necessary that 2 or 3 of them meet the established cut-off point for the panel to be considered positive. For example, in the first panel, an individual whose EBC has levels, expressed in both by one, of p-cresol less than 0.0143707, of cumyl alcohol greater than 0.172175 and of eicosenamide greater than 0.00450892, will be classified as a cancer patient, without import the levels of dehexadecyldane that you present.

BIBLIOGRAPHY

[I] A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman, Global cancer statistics, CA. Cancer J. Clin. 61 (2011) 69-90.

[2] P. Burney, A. Jithoo, B. Kato, C. Janson, D. Mannino, E. Nizankowska-Mogilnicka, et al., Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty: a bold analysis, Thorax. 69 (2014) 465-473.

[3] A. Jemal, M.M. Center, C. DeSantis, E.M. Ward, Global patterns of cancer incidence and mortality rates and trends, Epidemiol Cancer. Biomarkers Prev. 19 (2010) 1893-1907.

[4] P.M. Boiselle, Computed tomography screening for lung cancer, JAMA. 309 (2013) 1 163—1 170.

[5] O.M. Mets, M. Schmidt, C.F. Buckens, M.J. Gondrie, I. Isgum, M. Oudkerk, et al., Diagnosis of chronic obstructive pulmonary disease in lung cancer screening computed tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening, Respir. Res. 14 (2013) 59. [6] R.D. Beger, A review of applications of metabolomics in cancer., Metabolites. 3 (2013) 552-74.

[7] M. Calderón-Santiago, F. Priego-Capote, B. Jurado-Gámez, M.D. Luque de Castro, Optimization study for metabolomics analysis of human sweat by liquid chromatography- tandem mass spectrometry in high resolution mode, J. Chromatogr. A. 1333 (2014) 70-78. [8] M. Calderón-Santiago, F. Priego-Capote, N. Turck, X. Robín, B. Jurado-Gámez, J.C. Sánchez, et al., Human sweat metabolomics for lung cancer screening., Anal. Bioanal Chem. (2015).

[9] C.Y. Liu, C.H. Wang, T.C. Chen, H.C. Lin, C.T. Yu, H. P. Kuo, Increased level of exhaled nitric oxide and up-regulation of inducible nitric oxide synthase in patients with primary lung cancer., Br. J. Cancer. 78 (1998) 534-41.

[10] I. Horváth, Z. Lázár, N. Gyulai, M. Kollai, G. Losonczy, Exhaled biomarkers in lung cancer, Eur. Respir. J. 34 (2009) 261-275.

[II] GE Carpagnano, O. Resta, MP Foschino-Barbaro, E. Gramiccioni, F. Carpagnano, lnterleukin-6 is increased in breath condense of patients with non-small cell lung cancer., Int. J. Biol. Markers. 17 141-5. [12] DL Bayley, H. Abusriwil, A. Ahmad, RA Stockley, Validation of assays for inflammatory mediators in exhaled breath condense., Eur. Respir. J. 31 (2008) 943-8.

[13] E. Dalaveris, T. Kerenidi, A. Katsabeki-Katsafli, T. Kiropoulos, K. Tanou, K.I. Gourgoulianis, et al., VEGF, TNF-alpha and 8-isoprostane levéis in exhaled breath condense and serum of patients with lung cancer, Lung Cancer. 64 (2009) 219-225.

[14] K. Kostikas, G. Papatheodorou, K. Psathakis, P. Panagou, S. Loukides, Oxidative stress in expired breath condense of patients with COPD., Chest. 124 (2003) 1373-80.

[15] C. Gessner, S. Hammerschmidt, H. Kuhn, H.-J. Seyfarth, U. Sack, L. Engelmann, et al., Exhaled breath condense acidification in acute lung injury., Respir. Med. 97 (2003) 1188-94.

[16] B. Antus, I. Barta, Exhaled breath condense pH in patients with lung cancer., Lung Cancer. 75 (2012) 178-80.

[17] M. Phillips, K. Gleeson, J.M. Hughes, J. Greenberg, R.N. Cataneo, L. Baker, et al., Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study., Lancet. 353 (1999) 1930-3.

[18] M. Phillips, R.N. Cataneo, A.R.C. Cummin, A.J. Gagliardi, K. Gleeson, J. Greenberg, et al., Detection of lung cancer with volatile markers in the breath, Chest. 123 (2003) 2115-223.

[19] M. Phillips, N. Altorki, J.H.M. Austin, R.B. Cameron, R.N. Cataneo, J. Greenberg, et al., Prediction of lung cancer using volatile biomarkers in breath, Cancer Biomark. 3 (2007) 95-109.

[20] Y. Wang, Y. Hu, D. Wang, K. Yu, L. Wang, Y. Zou, et al., The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines. , Biomark Cancer. 11 (2012) 129-37. [21] G. Hedlin, J. Konradsen, A. Bush, An update on paediatric asthma, Eur. Respir. Rev. 21 (2012) 175-185.

[22] G. Hillas, S. Loukides, K. Kostikas, P. Bakakos, Biomarkers obtained by non-invasive methods in patients with COPD: where do we stand, what do we expect ?, Curr. Med. Chem. 16 (2009) 2824-2838. [23] P. Kubáñ, F. Foret, Exhaled breath condense: determination of non-volatile compounds and their potential for clinical diagnosis and monitoring. A review, Anal. Chim. Minutes 805 (2013) 1-18.

[24] C. Costa, C. Bucea, M. Bergallo, P. Solidoro, G. Rolla, R. Cavallo, Unsuitability of exhaled breath condense for the detection of herpesviruses DNA in the respiratory tract, J. Virol. Methods 173 (2011) 384-386.

[25] S. Dragonieri, P. Brinkman, E. Mouw, A.H. Zwinderman, P. Carratú, O. Resta, et al., An electronic nose discriminates exhaled breath of patients with untreated pulmonary sarcoidosis from controls, Respir. Med. 107 (2013) 1073-1078. [26] J. Hunt, Exhaled breath condense: An evolving tool for noninvasive evaluation of lung disease, J. Allergy Clin. Immunol 110 (2002) 28-34.

[27] A. Mazzatenta, C. Di Giulio, M. Pokorski, Pathologies currently identified by exhaled biomarkers, Respir. Physiol Neurobiol 187 (2013) 128-134.

[28] F. Hoffmeyer, M. Raulf-Heimsoth, V. Harth, J. Bünger, T. Brüning, Comparative analysis of selected exhaled breath biomarkers obtained with two different temperature-controlled devices, BMC Pulm. Med. 9 (2009) 48.

[29] G. Salvioli, R. Lugli, J.M. Pradelli, Relationships between squalene and cholesterol in bile: Effect of ursodeoxycholic acid administration in patients with radiolucent gallstones, Metabolism. 33 (1984) 641-645. [30] M. Phillips, J. Greenberg, Method for the collection and analysis of volatile compounds in the breath, J. Chromatogr. B Biomed. Sci. Appl. 564 (1991) 242-249.

[31] M. Phillips, Variation in volatile organic compounds in the breath of normal humans, J. Chromatogr. B. 729 (1999) 75-88.

[32] M. Phillips, N. Altorki, J.H.M. Austin, R.B. Cameron, R.N. Cataneo, R. Kloss, et al., Detection of lung cancer using weighted digital analysis of breath biomarkers., Clin. Chim. Minutes 393 (2008) 76-84.

[33] Smoke filters, (1968).

[34] A. Nowak, Z. Libudzisz, Influence of phenol, p-cresol and nature on growth and survival of intestinal lactic acid bacteria., Anaerobe. 12 (2006) 80-4. [35] ME Snook, RF Arrendale, HC Higman, OT Chortyk, Isolation of índoles and carbazoles from cigarette smoke condense, Anal. Chem. 50 (1978) 88-90.

[36] CE. Garner, S. Smith, B. de Lacy Costello, P. White, R. Spencer, C.S.J. Probert, et al., Volatile organic compounds from feces and their potential for diagnosis of gastrointestinal disease., FASEB J. 21 (2007) 1675-88.

[37] B. de Lacy Costello, A. Amann, H. Al-Kateb, C. Flynn, W. Filipiak, T. Khalid, et al., A review of the volatiles from the healthy human body., J. Breath Res. 8 (2014) 014001.

[38] J.J. Kabara, D.M. Swieczkowski, A.J. Conley, J.P. Truant, Fatty acids and derivatives as antimicrobial agents, Antimicrob. Chemother Agents 2 (1972) 23-28. [39] R.A. Stein, J.F. Mead, Small hydrocarbons formed by the peroxidation of squalene, Chem. Phys. Lipids. 46 (1988) 1 17-120.

[40] R.B. Woodward, K. Bloch, The cyclization of squalene in cholesterol sinthesis, J. Am. Chem. Soc. 75 (1953) 2023-2024.

Claims

1. - Use of the metabolites p-cresol, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-t-butylphenol, monostetin, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1-palmitate, benzyl alcohol and 2,4-diphenyl-4-methyl-2-E-pentane for diagnosis , classification and monitoring of cancer.

2. - The use of metabolites according to the preceding claim, wherein the cancer is lung cancer.

3. - A method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject that potentially suffers from cancer that comprises: a) quantifying the metabolites p-cresol, cumyl alcohol, eicosenamide and hexadecylindane in a sample biological isolated from said individual.

4. - The method according to the preceding claim, further comprising: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals .

5. - The method according to any of claims 3-4, wherein the cancer is lung cancer.

6. - The method according to any of claims 3-5, wherein the biological sample isolated from step (a) is exhaled air.

7- The method according to any of claims 3-6, wherein the biological sample isolated from step a) is condensed from exhaled air.

8. The method according to any of claims 3-7, wherein the quantification of the metabolites of step (a) is performed by analysis by GC-TOF / MS, preferably with electronic impact ionization (IE).

9. A method of diagnosis, classification and monitoring of cancer, comprising steps (a) - (b) according to any of claims 3-8, which further comprises: c) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites are detected, preferably of the 4 metabolites of step (a), in amounts significantly different from the reference amount.

10. - The method according to the preceding claim, wherein the cancer is lung cancer.

1 1. - A method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject potentially suffering from cancer that comprises: a) quantifying the metabolites 2,4-bis-dimethylbenzyl-6-t-butylphenol, monosterate, spiro-2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol and methyl stearate in a biological sample isolated from said individual.

12. - The method according to the preceding claim, further comprising: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in individuals who They have at least one risk factor for cancer, preferably smoking or with COPD.

13. - The method according to any of claims 11-12, wherein the cancer is lung cancer.

14. - The method according to any of claims 11-13, wherein the biological sample isolated from step (a) is condensed exhaled air.

15. The method according to any of claims 11-14, wherein the biological sample isolated from step a) is condensed with exhaled air.

16. - The method according to any of claims 1 1-15, wherein the quantification of the metabolites of step (a) is performed by analysis by GC-TOF / MS, preferably with electronic impact ionization (IE).

17. - A method of diagnosis, classification and monitoring of cancer, comprising steps (a) - (b) according to any of claims 1 1-16, further comprising: c) including the individual from step a) in the group of individuals presenting with cancer, when levels of at least 3 metabolites are detected, preferably of 4 metabolites, and more preferably of 5 metabolites of step (a), in significantly different amounts of reference amount.

18. - The method according to the preceding claim, wherein the cancer is lung cancer.

19. - A method of obtaining useful data for the diagnosis, classification and monitoring of an individual or subject that potentially suffers from cancer that includes: a) quantify the glycerol-1-palmitate metabolites, benzyl alcohol, monostetin, 2,4-diphenyl-4-methyl-2-E-pentane and p-cresol in a biological sample isolated from said individual.

20. - The method according to the preceding claim, further comprising: b) comparing the amounts obtained in step (a) with a reference amount, where the reference amount for each metabolite is the average levels of said metabolites in healthy individuals .

21. - The method according to any of claims 19-20, wherein the cancer is lung cancer.

22. The method according to any of claims 19-21, wherein the biological sample isolated from step (a) is exhaled air.

23- The method according to any of claims 19-22, wherein the biological sample isolated from step a) is condensed from exhaled air.

24. - The method according to any of claims 19-23, wherein the quantification of the metabolites of step (a) is performed by analysis by GC-TOF / MS, preferably with electronic impact ionization (IE).

25. - A method of diagnosis, classification and monitoring of cancer, comprising steps (a) - (b) according to any of claims 19-23, further comprising: c) including the individual from step a) in the group of individuals at high risk for cancer, when levels of at least 2 metabolites are detected, preferably 3 metabolites, more preferably 4 metabolites, and even more preferably 5 metabolites in step (a), in significantly different amounts of reference amount .

26. - The method according to the preceding claim, wherein the cancer is lung cancer.

27. - A kit or device comprising the elements necessary to quantify p-cresol metabolites, cumyl alcohol, eicosenamide, hexadecyldane, 2,4-bis-dimethylbenzyl-6-t-butylphenol, monostetin, spiro -2,4-heptane-1, 5-6-methylene, 13-heptadecin-1-ol, methyl stearate, glycerol-1-palmitate, benzyl alcohol and / or 2,4-diphenyl-4 -methyl-2-E-pentane, as described in any of claims 3-27.

28. - Use of the kit or device according to the preceding claim, for the diagnosis, classification and monitoring of cancer.

29.- The use of the kit or device according to the preceding claim, wherein the cancer is lung cancer.