Methods and kits for identification, assessment, prevention and therapy of lung diseases, including sexuality-based identification, assessment, prevention and therapy of diseases

Abstract

To provide methods and kits for identification, assessment, prevention and therapy of lung diseases, including sexuality-based disease identification, assessment, prevention and therapy.SOLUTION: The invention provides biomarkers and combinations of biomarkers that are useful in diagnosing lung diseases such as non-small cell lung cancer or reactive airway disease. The invention also provides methods of differentiating lung disease, methods of monitoring therapy, and methods of predicting a subject's response to therapeutic intervention based on the extent of expression of the biomarkers and combinations of biomarkers. Kits comprising agents for detecting the biomarkers and combination of biomarkers are also provided.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] (a) Technical Field

[0001] The present invention relates to the detection, identification, evaluation, prevention, and diagnosis of lung diseases using biomarkers. The present invention relates to methods and treatments for non-small cell lung cancer and reactive airway disease, as well as kits for the treatment. More specifically, the present invention relates to the diagnosis of non-small cell lung cancer and reactive airway disease by measuring and quantifying the expression levels of specific biomarkers. The present invention also relates to the identification of biomarkers present in human serum or other biological fluids that, when found to be expressed at levels different from those found in normal populations, are indicative of pathologies associated with human lung tissue and the human respiratory system. By identifying biomarkers associated with such pathologies, quantifying the expression levels of these biomarkers, and comparing the expression levels to levels generally expected to be present in normal human serum, it is possible to detect the presence of the pathologies early in their progression with a simple blood test, characterize their progression, and similarly distinguish between pathologies. [Background technology]

[0002] (b) Description of related technology Respiratory conditions such as asthma and lung cancer affect millions of Americans. In fact, the American Lung Association® reports that nearly 20 million Americans suffer from asthma. The American Cancer Society estimated 229,400 new cases of respiratory cancer and 164,840 deaths from respiratory cancer in 2007 alone. While the five-year survival rate for all cancer cases detected while the cancer is still localized is 46%, the five-year survival rate for lung cancer patients is only 13%. Correspondingly, only 16% of lung cancers are detected before the disease spreads. Lung cancer is generally classified into two major types based on the pathology of the cancer cells. Each type is named for the type of cell that transforms to become cancerous. Small cell lung cancer originates from small cells in human lung tissue, while non-small cell lung cancer generally encompasses all lung cancers that are not of the small cell type. Non-small cell lung cancer is classified together because treatment is generally the same for all non-small cell types. Furthermore, non-small cell lung cancer, or NSCLC overall, comprises approximately 75% of all lung cancers.

[0003]

[0003] A major factor in the poor survival rate of lung cancer patients is the difficulty in diagnosing the disease early. The fact is that current methods for diagnosing lung cancer or identifying its presence in humans are limited to x-rays of the lungs, computerized tomography (CT) scans, and similar tests to physically determine the presence or absence of a tumor. Thus, lung cancer is often diagnosed only in response to symptoms that have been present for a significant amount of time, and after the disease has been present in humans long enough to produce a physically detectable mass.

[0004] Similarly, current methods of detecting asthma typically involve recurrent wheezing, coughing, and chest pain. It is performed after the presentation of prolonged symptoms such as a feeling of tightness in the lungs. Current methods for detecting asthma are typically limited to lung function tests such as spirometry or stress testing. Furthermore, these tests are often ordered by physicians along with many other tests to rule out other conditions or reactive airway diseases such as chronic obstructive pulmonary disease (COPD), bronchitis, pneumonia, and congestive heart failure.

[0005]

[0005] A simple and reliable method for diagnosing human lung tissue pathology at an early stage of its development is currently There is no known method in the art. Furthermore, there are no blood tests available today that can indicate the presence of specific lung tissue pathologies. Therefore, it would be desirable to develop a method for determining the presence of lung cancer early in the progression of the disease. It would also be desirable to develop a method for diagnosing asthma and non-small cell lung cancer and distinguishing them from each other and from other lung diseases, such as infections, at the earliest appearance of symptoms. It would further be desirable to identify specific proteins present in human blood that, when altered in relative intensity of expression, are indicative of the presence of non-small cell lung cancer and / or reactive airway disease. Summary of the Invention

[0006] The present invention relates to the diagnosis of a subject's condition related to a pulmonary disease such as non-small cell lung cancer or reactive airway disease. A number of biomarkers have been identified that are useful for characterizing physical conditions. These biomarkers are shown in Tables 1-23.

[0007] Table 1A shows the expression levels of IL-1, IL-1, and IL-2 in individuals with one or more lung diseases. Table 1B lists biomarkers whose expression levels, when measured in individuals with either non-small cell lung cancer or reactive airway disease, were found to differ from levels in normal individuals, and which were found to exhibit differential expression levels between non-small cell lung cancer and reactive airway disease. Table 1C lists biomarkers whose expression, when measured in individuals with non-small cell lung cancer or reactive airway disease, was found to differ from levels in normal individuals.

[0008] Table 2 shows that the expression of IL-16 is positive when measured in individuals with reactive airway disease. Table 3 lists biomarkers whose expression was found to differ from levels in normal individuals when measured in individuals with non-small cell lung cancer. Table 4 lists biomarkers whose expression levels were found to differ when measured between individuals with non-small cell lung cancer and reactive airway disease.

[0009] Table 5A shows that expression levels in men with one or more lung diseases Table 5B lists biomarkers whose expression levels, when measured in men with either non-small cell lung cancer or reactive airway disease, were found to differ from levels in normal men, and which were found to show different expression levels between non-small cell lung cancer and reactive airway disease. Table 5C lists biomarkers whose expression, when measured in men with non-small cell lung cancer or reactive airway disease, was found to differ from levels in normal men.

[0010] Table 6 shows that its expression was positive when measured in men with reactive airway disease. Table 7 lists biomarkers whose expression, when measured in men with non-small cell lung cancer, was found to differ from levels in normal men. Table 8 lists biomarkers whose expression levels were found to differ between men with non-small cell lung cancer and reactive airway disease.

[0011] Table 9A shows that expression levels in women with one or more lung diseases Table 9B lists biomarkers whose expression levels, when measured in women with either non-small cell lung cancer or reactive airway disease, were found to differ from levels in normal women, and which were found to show differential expression levels between non-small cell lung cancer and reactive airway disease. Table 9C lists biomarkers whose expression, when measured in women with non-small cell lung cancer or reactive airway disease, was found to differ from levels in normal women.

[0012] Table 10 shows that expression, when measured in women with reactive airway disease, Table 11 lists biomarkers whose expression, when measured in women with non-small cell lung cancer, was found to be different from levels in normal women. Table 12 lists biomarkers whose expression levels were found to be different between women with non-small cell lung cancer and reactive airway disease.

[0013] Table 13A shows the expression of steroids that differ significantly between male and female reactive airway disease populations. Table 13B lists biomarkers whose expression does not significantly differ between male and female reactive airway disease populations. Table 14A lists biomarkers whose expression does not significantly differ between male and female non-small cell lung cancer populations. Table 14B lists biomarkers whose expression does not significantly differ between male and female non-small cell lung cancer populations. Table 15A lists biomarkers ranked by relative standard deviation of fluorescence intensity relative to a normal population. Table 15B lists biomarkers ranked by relative standard deviation of fluorescence intensity relative to a normal female population. Table 15C lists biomarkers ranked by relative standard deviation of fluorescence intensity relative to a normal male population.

[0014] Table 16A shows that expression levels of IL-16 in men with one or more lung diseases Table 16B lists biomarkers whose expression levels were found to differ from levels in normal men when measured in men with either non-small cell lung cancer or reactive airway disease, and which were found to show differential expression levels between non-small cell lung cancer and reactive airway disease. Table 16C lists biomarkers whose expression was found to differ from levels in normal men when measured in men with non-small cell lung cancer and reactive airway disease.

[0015] Table 17 shows that expression, when measured in men with reactive airway disease, Table 18 lists biomarkers whose expression, when measured in men with non-small cell lung cancer, was found to be different from that in normal men. Table 19 lists biomarkers whose expression levels were found to be different between men with non-small cell lung cancer and reactive airway disease.

[0016] Table 20A shows that expression levels of IL-1, IL-1, and IL-2 in women with one or more lung diseases Table 20B lists biomarkers whose expression levels, when measured in women with either non-small cell lung cancer or reactive airway disease, were found to differ from expression levels in normal women, and which were found to show differential expression levels between non-small cell lung cancer and reactive airway disease. Table 20C lists biomarkers whose expression, when measured in women with non-small cell lung cancer and reactive airway disease, was found to differ from expression levels in normal women.

[0017] Table 21 shows that expression, when measured in women with reactive airway disease, Table 22 lists biomarkers whose expression, when measured in women with non-small cell lung cancer, was found to be different from levels in normal women. Table 23 lists biomarkers whose expression levels were found to be different between women with non-small cell lung cancer and reactive airway disease.

[0018] The significance of Tables 1-15 was determined using the Student's t-test. Table 16-2 Significance of 3 was determined using the Kruskal-Wallis method.

[0019] A polypeptide comprising SEQ ID NO: 1-17 may be expressed by one or more These are further biomarkers that have been found to be altered by various diseases.

[0019]

[0020] The present invention provides a variety of diagnostic, prognostic and therapeutic methods that rely on the identification of these biomarkers. Provide the law.

[0021] The present invention relates to the use of any of Tables 1-12 or 16-23 in a physiological sample from a subject. The present invention provides a method of physiological characterization in a subject, comprising determining the level of expression of at least one biomarker from a number of Tables 1-12 or 16-23, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease, or can aid in identifying a lung disease such as non-small cell lung cancer or reactive airway disease. The present invention further provides a method of physiological characterization in a subject, comprising determining the level of expression of at least one biomarker from Tables 13B, 14B, or 15B, also appearing in Tables 1-12 or 16-23, in a physiological sample from the subject, preferably the biomarker is at least one of the biomarkers numbered 1-10 in Tables 1-12 or 16-23, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease. Alternatively or additionally, the level of expression of primary interactors of these biomarkers can be determined.

[0020]

[0022] The present invention provides a method for determining the degree of expression of SEQ ID NO: 12 in a physiological sample of a subject. wherein the degree of expression of SEQ ID NO: 12 is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease.

[0021]

[0023] The present invention relates to a method for detecting a compound selected from the group consisting of SEQ ID NOs: 1-17 in a physiological sample of a subject. and determining the degree of expression of at least one biomarker from any of Tables 1-12 or 16-23, wherein the degree of expression of said at least one polypeptide and at least one biomarker from any of Tables 1-12 or 16-23 is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease.

[0022]

[0024] The present invention provides the results of the analysis of Tables 2, 6, 10, 17 and 21 in physiological samples of subjects. The present invention provides a method for diagnosing reactive airway disease in a subject, comprising determining the degree of expression of at least one biomarker selected from the group consisting of:

[0023]

[0025] The present invention relates to a method for detecting a compound of Table 3, Table 7, Table 11, Table 18, or Table 22 in a physiological sample from a subject. and determining the level of expression of at least one biomarker from the at least one gene encoding a marker for non-small cell lung cancer, wherein the level of expression of the at least one biomarker is indicative of the presence or development of non-small cell lung cancer.

[0024]

[0026] The present invention relates to a method for treating a subject at risk for at least one of non-small cell lung cancer or reactive airway disease. Provided is a diagnostic method for aiding in distinguishing the likelihood of a subject being at risk for non-small cell lung cancer or reactive airway disease, comprising determining the degree of expression of at least one biomarker from Table 4, Table 8, Table 12, Table 19, or Table 23 in a physiological sample of the subject, wherein the degree of expression of at least one biomarker from Table 4, Table 8, Table 12, Table 19, or Table 23 aids in distinguishing the likelihood that the subject is at risk for non-small cell lung cancer or reactive airway disease.

[0025]

[0027] The present invention provides a method for detecting at least one of the compounds described herein in a physiological sample from a subject. Provided is a method for predicting the likelihood that a subject will respond to a therapeutic intervention, comprising determining the degree of expression of a biomarker, wherein the degree of expression of said at least one biomarker aids in predicting the subject's response to said therapeutic intervention.

[0026]

[0028] The present invention further provides a method for detecting at least one of the compounds described herein in a physiological sample from a subject. The present invention provides a method for monitoring a subject, comprising determining a first degree of expression of at least one biomarker, a second degree of expression of said at least one biomarker in a physiological sample of the subject at a time after said first determination, and comparing said first degree of expression and second degree of expression.

[0027]

[0029] The present invention further provides a method for detecting at least one biomarker as described herein. selecting a means for determining the degree of expression of said at least one biomarker, and designing a kit comprising said means for determining the degree of expression.

[0028]

[0030] The present invention further provides a method for detecting at least one biomarker as described herein. selecting a detection agent for detecting said at least one biomarker; and designing a kit comprising the detection agent for detecting said at least one biomarker.

[0029]

[0031] The present invention further comprises at least one biomarker described herein. A kit is provided.

[0032] The present invention further provides at least one polypeptide selected from the group consisting of SEQ ID NO: 12. The present invention provides a kit comprising a means for determining the degree of expression of a peptide.

[0030]

[0033] The present invention further provides at least one polypeptide selected from the group consisting of SEQ ID NO: 12. A kit is provided that comprises a detection agent for detecting the peptide.

[0034] The present invention further provides (a) at least one molecule selected from the group consisting of SEQ ID NOs: 1-17. and (b) means for determining the degree of expression of at least one biomarker from any of Tables 1-12 or Tables 16-23.

[0031]

[0035] The present invention further provides (a) at least one molecule selected from the group consisting of SEQ ID NOs: 1-17. and (b) a detection agent for detecting at least one biomarker from any of Tables 1-12 or Tables 16-23.

[0032]

[0036] The present invention further includes biomarkers and / or polypeptides from a plurality of the above tables. A kit comprising: The present invention provides, for example, the following items. (Item 1) 1. A method of physiological characterization of a subject, comprising determining the degree of expression of at least one biomarker from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the foregoing tables, in a physiological sample from the subject, wherein the degree of expression of the at least one biomarker is indicative of pulmonary disease. (Item 2) 1. A method of physiological characterization of a subject, comprising determining the level of expression of at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17 in a physiological sample of the subject, and identifying at least one polypeptide from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the foregoing tables, in the physiological sample of the subject. determining the degree of expression of a biomarker, wherein the degree of expression of the at least one polypeptide and the degree of expression of the at least one biomarker from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the tables, is indicative of pulmonary disease. (Item 3) A method for physiological characterization of a subject, comprising determining the degree of expression of SEQ ID NO: 12 in a physiological sample from the subject, wherein the degree of expression of the at least one polypeptide is indicative of a lung disease. (Item 4) 10. The method of claim 1, wherein said physiological characterization is diagnosing reactive airway disease in a subject comprising determining the degree of expression of at least one biomarker from Table 2, Table 6, Table 10, Table 17, or Table 21 in a physiological sample of the subject, wherein the degree of expression of the at least one biomarker is indicative of reactive airway disease. (Item 5) 2. The method of claim 1, wherein said physiological characterization is diagnosing non-small cell lung cancer in a subject comprising determining the degree of expression in the subject of at least one biomarker from Table 3, Table 7, Table 11, Table 18, or Table 22 in a physiological sample from the subject, wherein the degree of expression of the at least one biomarker is indicative of the presence or development of non-small cell lung cancer. (Item 6) 10. The method of claim 1, wherein said physiological characterization comprises determining the degree of expression of at least one biomarker from Table 4, Table 8, Table 12, Table 19, or Table 23 in a physiological sample of the subject, wherein the degree of expression of the at least one biomarker aids in diagnosing the pulmonary disease. (Item 7) 7. The method of any one of items 1-2 and 4-6, wherein the at least one biomarker comprises a plurality of biomarkers. (Item 8) 7. The method of items 1-2 and 4-6, wherein the at least one biomarker is selected from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of the tables, preferably the plurality of biomarkers comprises a plurality of biomarkers selected from biomarkers numbered 1-20, more preferably biomarkers numbered 1-10, of Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of the tables. (Item 9) 9. The method of claim 8, further comprising determining whether the at least one biomarker is further found in Table 13B and / or Table 14B. (Item 10) 7. The method of any one of items 1-2 and 4-6, wherein the human is male and the biomarkers are selected from Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, or a combination of the tables, preferably the plurality of biomarkers are selected from biomarkers numbered 1-20, more preferably biomarkers numbered 1-10 of Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, or a combination of the tables. (Item 11) 7. The method of any one of items 1-2 and 4-6, wherein the human is a female and the at least one biomarker is selected from Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables, preferably the plurality of biomarkers are selected from biomarkers numbered 1-20, more preferably biomarkers numbered 1-10, of Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables. (Item 12) 12. The method of any one of items 10 or 11, further wherein said at least one biomarker is further found on Table 13A and / or Table 14A. (Item 13) 13. The method of physiological characterization of any one of paragraphs 1-12, further comprising determining the degree of expression of at least one primary interactor linked to a biomarker from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23 in a physiological sample from the subject, wherein the degree of expression of the at least one primary interactor is indicative of pulmonary disease. (Item 14) 14. The method of any one of items 1-3 and 6-13, wherein the lung disease is non-small cell lung cancer or reactive airway disease. (Item 15) 15. The method of item 4 or 14, wherein the reactive airway disease is asthma. (Item 16) 16. The method of any one of items 1-15, wherein determining the degree of expression is performing a quantitative multiplex immunoassay. (Item 17) 17. The method of any one of items 1-16, wherein the method further comprises obtaining a physiological sample from the subject. (Item 18) 18. The method according to any one of items 1-17, wherein the physiological sample is a biological fluid such as blood serum or plasma. (Item 19) 19. The method of any one of items 1-18, wherein the subject is a mammal such as a human. (Item 20) 1. A method for physiological characterization of a subject, comprising: (a) determining whether the level of MMP-8 in a subject is abnormal; and (b) determining whether the level of a biomarker selected from the group consisting of hepatocyte growth factor (HGF), soluble Fas ligand (sFasL), PAI-1, insulin (INS), epidermal growth factor (EGF), myeloperoxidase (MPO), macrophage migration inhibitory factor (MIF), and combinations thereof in the subject is abnormal; wherein an abnormal level of the biomarker is indicative of pulmonary disease. (Item 21) 22. The method of claim 21, further comprising a step of determining whether the level of MMP-1 in the subject is abnormal. (Item 22) 22. The method of any one of items 20 or 21, wherein step (b) comprises determining whether the level of HGF in the subject is abnormal. (Item 23) 1. A method for physiological characterization of a subject, comprising: (a) measuring the level of MMP-8 or a primary interactor thereof in a subject; and (b) measuring in the subject the level of a biomarker selected from the group consisting of HGF, sFasL, PAI-1, INS, EGF, MPO, and MIF or a primary interactor associated therewith; wherein if the level of the biomarker or the primary interactor linked thereto is abnormal, the subject has an increased likelihood of having a lung disease. 24. The method of claim 23, further comprising measuring a level of MMP-1 or a first interactor associated therewith in the subject, wherein if the level of the biomarker or the first interactor associated therewith is abnormal, the subject has an increased likelihood of having lung disease. (Item 25) 25. The method of any one of items 23 or 24, wherein step (b) comprises measuring the level of HGF in the subject. (Item 26) 1. A method for physiological characterization of a male subject, comprising: (a) determining the levels of at least two biomarkers of INS, MMP-7, MMP-8, resistin, and / or HGF in said male subject; and (b) comparing the levels determined in step (a) with the levels of said at least two biomarkers in a normal subject; wherein the comparison allows for determining whether each biomarker is up- or down-regulated in the subject, and further wherein if INS is down-regulated and / or MMP-7, MMP-8, resistin, and / or HGF are up-regulated, the male subject has an increased likelihood of having lung disease. (Item 27) 27. The method according to item 26, wherein step (a) comprises determining the levels of at least three, preferably at least four, more preferably at least five of said biomarkers. (Item 28) 1. A method for physiological characterization of a female subject, comprising: (a) determining the levels of at least two of IL-8, serum amyloid P, serum amyloid A, and C-reactive protein in said female subject; and (b) comparing the levels determined in step (a) with the levels of said at least two biomarkers in a normal subject; wherein the comparison allows for determining whether each biomarker is up- or down-regulated in the patient, and further wherein if IL-8 and / or serum amyloid P are down-regulated and / or serum amyloid A and / or C-reactive protein are up-regulated, the female subject has an increased likelihood of having lung disease. (Item 29) 27. The method of claim 26, wherein step (a) comprises determining the levels of at least three, preferably at least four, of said biomarkers. (Item 30) (a) selecting a plurality of biomarkers from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of said tables; (b) selecting a means for determining the degree of expression of said biomarkers; and (c) designing a kit comprising said means for determining the degree of expression; 10. A method for designing a kit for diagnosing a lung disease in a subject, comprising: (Item 31) (a) selecting at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17, and at least one biomarker from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the foregoing tables; (b) selecting at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17, and at least one biomarker from Table 1A, Table 1B, 21. A method for designing a kit for diagnosing lung disease in a subject, comprising: (a) selecting a means for determining the degree of expression of said at least one biomarker from Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of said tables; and (c) designing a kit comprising said means for determining the degree of expression. (Item 32) 32. The method of item 30 or 31, wherein the biomarkers are selected from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of said tables, preferably the biomarkers are selected from a plurality of biomarkers from biomarker numbers 1-20, more preferably biomarkers 1-10 of Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of said tables. (Item 33) 33. The method of any one of items 30-32, further comprising selecting at least one biomarker from Table 13B and / or Table 14B. (Item 34) 32. The method of any one of items 30 or 31, wherein the subject is male and the biomarkers are selected from Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, or a combination of the said tables, preferably the plurality of biomarkers are selected from biomarkers numbered 1-20, more preferably biomarkers numbered 1-10 of Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, or a combination of the said tables. (Item 35) 32. The method of any one of items 30 or 31, wherein the subject is female and the at least one biomarker is selected from Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables, preferably the plurality of biomarkers are selected from biomarkers 1-20, more preferably biomarkers 1-10 of Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables. (Item 36) 37. The method of any one of items 35 or 36, further comprising selecting at least one biomarker from Table 13A and / or Table 14A. (Item 37) 10. A kit comprising means for determining the degree of expression of a plurality of biomarkers from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of said tables. (Item 38) 10. A kit comprising: (a) a means for determining the degree of expression of at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17; and (b) a means for determining the degree of expression of at least one biomarker from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the foregoing tables. (Item 39) 39. The kit of item 37 or 38, wherein the at least one biomarker is selected from Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of the said tables, preferably the biomarkers are selected from a plurality of biomarkers from biomarker numbers 1-20, more preferably biomarkers 1-10 of Table 1A, Table IB, Table 1C, Table 2, Table 3, Table 4, or a combination of the said tables. (Item 40) The kit according to any one of Items 37-39, further comprising means for determining the degree of expression of at least one biomarker from Table 13B and / or Table 14B. (Item 41) 39. The kit of item 37 or 38, wherein the at least one biomarker is selected from Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, or a combination of the said tables, preferably the biomarkers are selected from a plurality of biomarkers from biomarkers numbered 1-20, more preferably biomarkers numbered 1-10 of Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, or a combination of the said tables. (Item 42) 39. The kit according to item 37 or 38, wherein the at least one biomarker is selected from Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables, preferably the biomarkers are selected from a plurality of biomarkers from biomarkers 1-20, more preferably biomarkers 1-10, of Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20A, Table 20B, Table 20C, Table 21, Table 22, Table 23, or a combination of the said tables. (Item 43) 43. The kit according to item 41 or 42, further comprising means for determining the degree of expression of at least one biomarker from Table 13A and / or Table 14A. (Item 44) 44. The kit of any one of items 37-43, wherein the biomarker is a polypeptide. (Item 45) 45. The kit according to any one of items 37-44, wherein the means for determining the degree of expression comprises a detection agent. (Item 46) 46. The kit of item 45, wherein the detection agent comprises a reagent that specifically binds to the biomarker or the polypeptide. (Item 47) 47. The kit of claim 46, wherein the reagent comprises an antibody. [Brief explanation of the drawings]

[0033]

[0037] [Figure 1A] FIG. 1A shows the mean fluorescence intensity levels of the biomarkers in the normal (NO) population from Example 1, as well as the standard deviation and relative standard deviation.

[0038] [Figure 1B] FIG. 1B shows the mean fluorescence intensity levels of the biomarkers in the non-small cell lung cancer (LC) population from Example 1, as well as the standard deviation and relative standard deviation.

[0034]

[0039] [Figure 1C]FIG. 1C shows the mean fluorescence intensity levels of the biomarkers in the asthmatic (AST) population from Example 1, as well as the standard deviation and relative standard deviation.

[0040] [Figure 1D] FIG. 1D shows the percent change in the mean fluorescence intensity of each biomarker in the LC vs. NO, AST vs. NO, and LC vs. AST populations from Example 1.

[0035]

[0041] [Figure 1E] Figure 1E shows the probability associated with the Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the populations from Example 1, where the populations compared are the LC population vs. the NO population, the AST population vs. the NO population, and the LC population vs. the AST population, respectively.

[0036]

[0042] [Figure 2A] FIG. 2A shows the mean fluorescence intensity levels of the biomarkers in the normal (NO) population from Example 2, as well as the standard deviation and relative standard deviation.

[0043] [Figure 2B] FIG. 2B shows the mean fluorescence intensity levels of the biomarkers in the non-small cell lung cancer (LC) population from Example 2, as well as the standard deviation and relative standard deviation.

[0037]

[0044] [Figure 2C] FIG. 2C shows the mean fluorescence intensity levels of the biomarkers in the asthmatic (AST) population from Example 2, as well as the standard deviation and relative standard deviation.

[0045] [Figure 2D] FIG. 2D shows the percent change in the mean fluorescence intensity of each biomarker in the LC vs. NO, AST vs. NO, and AST vs. LC populations from Example 2.

[0038]

[0046] [Figure 2E]Figure 2E shows the probability associated with the Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the populations from Example 2, where the populations compared are the LC population vs. the NO population, the AST population vs. the NO population, and the AST population vs. the LC population, respectively.

[0039]

[0047] [Figure 3A] FIG. 3A shows the mean fluorescence intensity levels of the biomarkers in the normal (NO) population from Example 3, as well as the standard deviation and relative standard deviation.

[0048] [Figure 3B] FIG. 3B shows the mean fluorescence intensity levels of the biomarkers in the non-small cell lung cancer (LC) population from Example 3, as well as the standard deviation and relative standard deviation.

[0040]

[0049] [Figure 3C] FIG. 3C shows the mean fluorescence intensity levels of the biomarkers in the asthmatic (AST) population from Example 3, as well as the standard deviation and relative standard deviation.

[0050] [Figure 3D] FIG. 3D shows the percent change in the mean fluorescence intensity of each biomarker in the AST vs. NO, LC vs. NO, and AST vs. LC populations from Example 3.

[0041]

[0051] [Figure 3E] Figure 3E shows the probability associated with the Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the populations from Example 3, where the compared populations are the AST population vs. the NO population, the LC population vs. the NO population, and the AST population vs. the LC population, respectively.

[0042]

[0052] [Figure 4A]FIG. 4A shows the mean fluorescence intensity levels of the biomarkers in the population of normal (NO) women from Example 3, as well as the standard deviation and relative standard deviation.

[0053] [Figure 4B] FIG. 4B shows the mean fluorescence intensity levels of the biomarkers in a population of women with non-small cell lung cancer (LC) from Example 3, as well as the standard deviation and relative standard deviation.

[0043]

[0054] [Figure 4C] FIG. 4C shows the mean fluorescence intensity levels of the biomarkers in the population of asthmatic (AST) women from Example 3, as well as the standard deviation and relative standard deviation.

[0055] [Figure 4D] FIG. 4D shows the percent change in mean fluorescence intensity of each biomarker in the female AST vs. NO population, the female LC vs. NO population, and the female AST vs. LC population from Example 3.

[0044]

[0056] [Figure 4E] Figure 4E shows the probabilities associated with Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the female populations from Example 3, where the compared populations are the female AST population vs. NO population, the female LC population vs. NO population, and the female AST population vs. LC population, respectively.

[0045]

[0057] [Figure 5A] FIG. 5A shows the mean fluorescence intensity levels of the biomarkers in a population of normal (NO) men from Example 3, as well as the standard deviation and relative standard deviation.

[0058] [Figure 5B] FIG. 5B shows the mean fluorescence intensity levels of the biomarkers in a population of men with non-small cell lung cancer (LC) from Example 3, as well as the standard deviation and relative standard deviation.

[0046]

[0059] [Figure 5C] FIG. 5C shows the mean fluorescence intensity levels of the biomarkers in the population of asthmatic (AST) men from Example 3, as well as the standard deviation and relative standard deviation.

[0060] [Figure 5D] FIG. 5D shows the percent change in mean fluorescence intensity of each biomarker in the male AST vs. NO population, the male LC vs. NO population, and the male AST vs. LC population from Example 3.

[0047]

[0061] [Figure 5E] Figure 5E shows the probability associated with Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the male populations from Example 3, where the compared populations are the male AST vs. NO population, the male LC vs. NO population, and the male LC vs. AST population, respectively.

[0048]

[0062] [Figure 6A] Figure 6A shows the percent change in mean fluorescence intensity of each biomarker in the male AST population compared to the female AST population, the male LC population compared to the female LC population, and the male NO population compared to the female NO population from Example 3.

[0049]

[0063] [Figure 6B] Figure 6B shows the probabilities associated with Student's t-values obtained by comparing the mean fluorescence intensity and variability measured for each biomarker in the male and female populations from Example 3, where the compared populations are the male and female AST populations, the male and female LC populations, and the male and female NO populations, respectively.

[0050]

[0064] [Figure 7A]FIG. 7A shows the percent change in mean concentration of each biomarker in the LC vs. NO, AST vs. NO, and AST vs. LC female populations of Example 3.

[0051]

[0065] [Figure 7B] Figure 7B shows the probability associated with the Kruskal-Wallis test calculated by comparing the concentrations measured for each biomarker in the female populations of Example 3, where the compared populations are the female AST vs. NO population, the female LC vs. NO population, and the female AST vs. LC population, respectively.

[0052]

[0066] [Figure 8A] FIG. 8A shows the percent change in mean concentration of each biomarker in the male LC vs. NO population, the male AST vs. NO population, and the male AST vs. LC population of Example 3.

[0053]

[0067] [Figure 8B] Figure 8B shows the probability associated with the Kruskal-Wallis test calculated by comparing the concentrations measured for each biomarker in the male populations of Example 3, where the compared populations are the male AST vs. NO population, the male LC vs. NO population, and the male AST vs. LC population, respectively.

[0054]

[0068] [Figure 9] FIG. 9 shows the relationships between the biomarkers in Table 16B. DETAILED DESCRIPTION OF THE INVENTION

[0055]

[0069] The present invention provides a method for detecting, identifying, assessing, preventing, and diagnosing gender-based diseases, and for treating The present invention relates to various methods for detecting, differentiating, evaluating, preventing, diagnosing, and treating lung diseases using biomarkers, including determining the level of expression of specific biomarkers, the altered expression of which is indicative of non-small cell lung cancer and / or reactive airway disease (e.g., asthma, chronic obstructive pulmonary disease, etc.). The present invention also provides various kits comprising detection agents for detecting these biomarkers or means for determining the level of expression of these biomarkers.

[0056] definition

[0070] As used herein, a "biomarker" or "marker" refers to a biological Biomarkers are biological molecules that can be objectively measured as characteristic indicators of the physiological state of a biological system. For purposes of this disclosure, biological molecules include ions, small molecules, peptides, proteins, peptides and proteins bearing post-translational modifications, nucleosides, nucleotides, and polynucleotides, including RNA and DNA, glycoproteins, lipoproteins, and various covalent and non-covalent modifications of these types of molecules. Biological molecules include any of these entities that are natural, characteristic, and / or essential to the function of a biological system. While most biomarkers are polypeptides, they can also be modified mRNAs, which are the pre-translational form of gene products expressed as mRNA or polypeptides, or they can include post-translational modifications of polypeptides.

[0057]

[0071] As used herein, "subject" means any animal, including Preferably, however, the subject is a mammal, such as a human. In many embodiments, the subject is a human patient who has or is at risk of having a pulmonary disease.

[0058]

[0072] As used herein, "physiological samples" refer to samples from biological fluids and tissues. Examples of biological fluids include whole blood, blood plasma, blood serum, sputum, urine, sweat, lymph, and bronchoalveolar lavage fluid. Examples of tissue samples include biopsies from solid lung tissue or other solid tissues, lymph node biopsies, and metastatic biopsies. Methods for obtaining physiological samples are known.

[0059]

[0073] As used herein, "therapeutic intervention" refers to the administration of one or more small molecules or includes the administration of a therapeutic agent such as a large molecule, radiation, surgery, or any combination thereof.

[0060]

[0074] As used herein, a "detection agent" refers to a biomarker as described herein. Detection agents include reagents and systems that specifically detect markers. Detection agents include reagents such as antibodies, nucleic acid probes, aptamers, lectins, or other reagents that have sufficient specific affinity for a particular marker or markers to discriminate between that particular marker and other markers in a sample of interest, as well as systems such as sensors, including sensors that use immobilized ligands by binding or other methods, as described above.

[0061] Biomarker identification

[0075] The biomarkers of the present invention were identified using two methods. First, Identification of biomarkers indicative of alveolar lung cancer and / or asthma was performed by comparing the measured expression levels of 59 selected biomarkers in the plasma of patients from a population diagnosed with each condition, as confirmed by a physician, with a population not diagnosed with the condition, as detailed in Examples 1-3.

[0062]

[0076] Second, biomarkers were identified using mass spectroscopy. Identification of proteins indicative of asthma and / or lung cancer was performed by comparing mass spectral data of tryptic peptide digests of samples obtained from patients with different physiological states. Specifically, the data were peptide fragment masses represented as a one-dimensional graphed indication of the intensity of the pseudo- or protonated molecular ion signals of peptides and proteins containing the fragments expressed over time. Expression levels of several thousand proteins were compared, resulting in the identification of 17 proteins that were expressed at significantly different intensities between populations of individuals without any diagnosed lung tissue pathology, individuals with asthma as diagnosed by a physician, and individuals with non-small cell lung cancer as diagnosed by a physician. This method is described in detail in Examples 6 and 7.

[0063] Primary interactor

[0077] Promotes and regulates the physiological functions of many cells and organisms necessary to sustain life. To control signaling, biological molecules must interact with one another. These interactions can be thought of as a type of communication, in which each type of biological molecule can be thought of as a message. As part of their signaling function, these molecules necessarily interact with a wide range of targets, including other types of biological molecules.

[0064]

[0078] One type of interacting molecule is commonly known as a receptor. Another type of intramolecular interaction is the binding of cofactors to enzymes. These intramolecular interactions form networks of signaling molecules that work together to perform and regulate essential life functions of cells and organisms. Certain biomarkers of the present invention physiologically bind to other biomarkers, the levels of which increase or decrease in a manner coordinated with the level of the particular biomarker. These other biomarkers are referred to as "primary interactors" with respect to a particular biomarker of the present invention.

[0065]

[0079] A "primary interactor" is a molecular entity that directly interacts with a particular biological molecule. For example, the drug morphine interacts directly with opiate receptors, ultimately resulting in the loss of pain sensation. Thus, opiate receptors are primary interactors under the definition of "primary interactor." Primary interactors include both upstream and downstream immediate neighbors of the biomarker in the communication pathways with which they interact. These entities include proteins, nucleic acids, and small molecules, which can be connected by relationships including, but not limited to, direct (or indirect) regulation, expression, chemical reaction, molecular synthesis, binding, promoter binding, protein modification, and molecular transport. Groups of biomarkers whose levels are coordinated are known to those skilled in the art and knowledgeable in physiology and cell biology. Indeed, primary interactors for specific biomarkers are known in the art and can be found using various databases and available bioinformatics software such as ARIADNE PATHWAY STUDIO, ExPASY Proteomics Server Qlucore Omics Explorer, Protein Prospector, PQuad, ChEMBL, etc. (e.g., ARIADNE PATHWAY STUDIO, Ariadne, Inc.,<www.ariadne.genomics.com> or ChEMBL Database, European Bioinformatics Institute, European Molecular Biology Laboratory,<www.ebi.ac.uk> (See

[0066]

[0080] When the level of a particular biomarker of the present invention is abnormal, its expression is associated with a particular The level of a primary interactor that is consistent with a biomarker is also abnormal. Thus, determining that the level of a particular biomarker is abnormal can be accomplished by measuring the level of its corresponding primary interactor. One skilled in the art will, of course, ensure that the level of a primary interactor used in place of or in addition to a particular biomarker changes in a defined and reproducible manner consistent with the behavior of the particular biomarker.

[0067]

[0081] The present invention provides a method for detecting specific biomarkers for any of the methods described herein. It is provided that methods performed with a particular biomarker can alternatively be performed with a primary interactor of that particular biomarker. For example, the present invention provides a method of physiological characterization comprising determining the level of expression of HGF. Similarly, the present invention provides a method of physiological characterization further comprising determining the level of expression of a primary interactor of HGF. Primary interactors of HGF include, but are not limited to, those defined in Example 12.

[0068] Table defining significant biomarkers

[0082] Table 1A shows the expression levels of AST vs. NO population, LC vs. NO population, and AST vs. Biomarkers with at least one significant or marginally significant difference between the LC populations are listed.

[0069] [Table 1A]

[0070]

[0083] Table 1B shows the expression levels of AST vs. NO population, LC vs. NO population, and AST Biomarkers with at least one significant difference between the paired LC populations are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0071] [Table 1B]

[0072]

[0084] Table 1C shows that the expression levels were significantly different between the AST vs. NO population and the LC vs. NO population. Biomarkers with significant or marginally significant differences are listed. Significance was determined using a Student's t-test as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0073] [Table 1C]

[0074]

[0085] Table 2 shows that the expression levels showed significant or marginally significant differences between the AST and NO populations. Biomarkers with significant differences are listed below. Significance was determined using Student's t as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0075] [Table 2]

[0076]

[0086] Table 3 shows that the expression levels showed significant or marginally significant differences between the LC and NO populations. Biomarkers that correlated with the phenotype are listed below. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0077] [Table 3]

[0078]

[0087] Table 4 shows that the expression levels showed significant or marginally significant differences between the AST and LC populations. Biomarkers with significant differences are listed below. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0079] [Table 4]

[0080]

[0088] Table 5A shows the expression levels of AST vs. NO males and LC vs. NO males. Biomarkers with significant or marginally significant differences in at least one of the populations, and between the AST vs. LC male populations, are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0081] [Table 5A]

[0082]

[0089] Table 5B shows the expression levels of AST vs. NO males and LC vs. NO males. Biomarkers with significant differences in at least one of the populations, and between the AST vs. LC male populations, are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0083] [Table 5B]

[0084]

[0090] Table 5C shows the expression levels of AST vs. NO males and LC vs. NO males. Biomarkers with significant or marginally significant differences between at least one of the populations are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0085] [Table 5C]

[0086]

[0091] Table 6 shows that the expression levels were significantly or marginally significant between the male populations for AST vs. NO. Biomarkers with significant differences are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0087] [Table 6]

[0088]

[0092] Table 7 shows that the expression levels were significantly or marginally significant between the LC vs. NO male populations. Biomarkers with significant differences are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0089] [Table 7]

[0090]

[0093] Table 8 shows that the expression levels were significantly or marginally significant between the AST vs. LC male populations. Biomarkers with significant differences are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0091] [Table 8]

[0092]

[0094] Table 9A shows the expression levels of AST vs. NO women, LC vs. NO women. Biomarkers with significant or marginally significant differences in at least one of the populations, and between the AST vs. LC women populations, are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the significance of fluorescence intensity and the magnitude of the difference.

[0093] [Table 9A]

[0094]

[0095] Table 9B shows the expression levels of AST vs. NO women, LC vs. NO women. Biomarkers with significant differences between the populations of women with AST and those with LC are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0095] [Table 9B]

[0096] Table 9C shows the expression levels of AST vs. NO women and LC vs. NO women. Biomarkers with significant or marginally significant differences between populations are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0097] [Table 9C]

[0098] Table 10 lists biomarkers whose expression levels had significant or marginally significant differences between the AST vs. NO female populations. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0099] [Table 10]

[0100] Table 11 shows that expression levels were significantly or marginally significant between the LC vs. NO female populations. Biomarkers with significant differences are listed. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0101] [Table 11]

[0102] Table 12 shows whether the expression levels were significantly or marginally different between the AST vs. LC female populations. Biomarkers with significant differences are listed. Significance was determined using a Student's t-test as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0103] [Table 12]

[0104] Table 13A lists biomarkers whose expression levels have significant or marginally significant differences between male and female AST populations. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0105] [Table 13A]

[0106] Table 13B lists biomarkers whose expression levels have non-significant differences between male and female AST populations. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0107] [Table 13B]

[0108] Table 14A lists biomarkers whose expression levels have significant or marginally significant differences between male and female LC populations. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0109] [Table 14A]

[0110] Table 14B lists biomarkers whose expression levels have non-significant differences between male and female LC populations. Significance was determined using a Student's t-test, as shown in Examples 1-3. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0111] [Table 14B]

[0112]

[0104] Table 15A lists the biomarkers ranked in ascending order by the relative standard deviation of fluorescence intensity relative to the normal population.

[0113] [Table 15A]

[0114]

[0105] Table 15B lists the biomarkers ranked in ascending order by the relative standard deviation of fluorescence intensity for a population of normal women.

[0115] [Table 15B]

[0116]

[0106] Table 15C lists the biomarkers ranked in ascending order by the relative standard deviation of fluorescence intensity for a population of normal males.

[0117] [Table 15C]

[0118] Table 16A lists biomarkers whose expression levels had significant differences in at least one of the AST vs. NO male populations, the LC vs. NO male populations, and the AST vs. LC male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the significance and magnitude of the difference in fluorescence intensity.

[0119] [Table 16A]

[0120] Table 16B lists biomarkers whose expression levels had significant differences between the AST vs. NO, LC vs. NO, and AST vs. LC male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0121] [Table 16B]

[0122] Table 16C lists biomarkers whose expression levels had significant differences between the AST vs. NO and LC vs. NO male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0123] [Table 16C]

[0124] Table 17 lists biomarkers whose expression levels had significant differences between the AST vs. NO male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0125] [Table 17]

[0126] Table 18 lists biomarkers whose expression levels differ significantly between the LC vs. NO male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0127] [Table 18]

[0128] Table 19 lists biomarkers whose expression levels differ significantly between the AST vs. LC male populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0129] [Table 19]

[0130] Table 20A lists biomarkers whose expression levels had significant differences in at least one of the AST vs. NO female populations, the LC vs. NO female populations, and the AST vs. LC female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the significance and magnitude of the difference in fluorescence intensity.

[0131] [Table 20A]

[0132] Table 20B lists biomarkers whose expression levels had significant differences between the AST vs. NO, LC vs. NO, and AST vs. LC female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0133] [Table 20B]

[0134] Table 20C lists biomarkers whose expression levels had significant differences between the AST vs. NO and LC vs. NO female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0135] [Table 20C]

[0136] Table 21 lists biomarkers whose expression levels had significant differences between the AST vs. NO female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0137] [Table 21]

[0138] Table 22 lists biomarkers whose expression levels were significantly different between the LC vs. NO female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0139] [Table 22]

[0140] Table 23 lists biomarkers whose expression levels have significant differences between the AST vs. LC female populations. Significance was determined using the Kruskal-Wallis method, as described in Example 4. Marginally significant biomarkers are not included. Markers are listed in descending order based on the magnitude of the difference in fluorescence intensity.

[0141] [Table 23]

[0142] Determination of the level of expression The degree of expression generally relates to the quantitative measurement of expression products, which are typically proteins or peptides. The present invention contemplates determining the degree of expression at the RNA (pre-translational) or protein level (including post-translational modifications). In particular, the present invention contemplates determining changes in the concentration of biomarkers that reflect increases or decreases in the levels of transcription, translation, post-translational modifications, or the extent or degree of protein degradation, where these changes are associated with a particular disease state or disease progression.

[0143]

[0120] Samples are taken to ensure that the degree of expression in the subject is proportional to the concentration of said marker in the sample. Measurements are made such that the measured value is proportional to the concentration of the biomarker in the sample. Thus, the measured value is proportional to the degree of expression. Selection of sample collection and measurement techniques that meet these requirements is within the skill of the art.

[0144] Typically, the degree of expression of at least one biomarker indicative of pulmonary disease is the level of the at least one biomarker that differs by a statistically significant amount from the average expression level in normal individuals; in other words, the at least one biomarker is a statistical outlier from normal. Statistical significance and deviation can be determined using any known method for comparing population means, or by comparing a measurement to a population mean. Such methods include Student's t-test, analysis of variance (ANOVA), and the like, for single and multiple markers considered simultaneously.

[0145]

[0122] As an alternative to, or in combination with, determining the degree of expression, the methods described herein include determining whether the level of a biomarker falls within a normal level (e.g., range) or is outside of normal levels (i.e., abnormal). Those who measure the levels of biological molecules in physiological samples routinely determine the normal level of a particular biomarker in the population they measure regularly, typically describing it as a normal range of values determined by a particular laboratory. Thus, one of ordinary skill in the art will necessarily be familiar with the normal level of a particular biomarker and will be able to determine whether the level of that biomarker is outside of a normal level or range.

[0146]

[0123] More typically, the degree of expression of at least one biomarker indicative of pulmonary disease is the level of the at least one biomarker that differs by a sufficient magnitude to be analytically significant from the average expression level in normal individuals, such that the difference can be determined to diagnose, predict, and / or assess pulmonary disease. Those skilled in the art will understand that a difference of greater magnitude is preferred to aid in the diagnosis, prediction, and / or assessment of pulmonary disease. See Instrumental Methods of Analysis, Seventh Edition, 1988.

[0147] Many proteins expressed by normal subjects are expressed to varying degrees in subjects with diseases or conditions such as small cell lung cancer or asthma. Those skilled in the art will recognize that most diseases manifest alterations in a number of different biomarkers. Thus, diseases can be characterized by patterns of expression of multiple markers. Indeed, changes in patterns of expression of multiple biomarkers can be used in a variety of diagnostic and prognostic methods, as well as in monitoring, therapy selection, and patient evaluation methods. The present invention provides such methods. These methods comprise determining the pattern of expression of multiple markers for a particular physiological state, or determining changes in such patterns that correlate with changes in the physiological state as characterized by any suitable technique for pattern recognition.

[0148]

[0125] Many methods for determining the level of expression are known in the art. Means for determining expression include, but are not limited to, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), high-pressure liquid chromatography with radiometric or spectroscopic detection by visible or ultraviolet light absorbance, qualitative and quantitative mass spectroscopy, Western blots, one- or two-dimensional gel electrophoresis with quantitative visualization by detection of radioactivity, fluorescent or chemiluminescent probes, or nuclei, antibody-based detection with absorbance or fluorometry, quantification by fluorescence of any of the many chemiluminescent reporter systems, enzyme assays, immunoprecipitation or immunocapture assays, solid-phase and liquid-phase immunoassays, protein arrays or chips, DNA arrays or chips, plate assays, assays using molecules with binding affinities that allow differentiation, such as aptamers and molecularly imprinted polymers, and any other suitable techniques, any other quantitative analytical determination of the concentration of a biomarker by instrumentation of any of the described detection techniques or instruments.

[0149]

[0126] The step of determining the degree of expression can be performed by any means for determining expression known in the art, particularly those discussed herein. In a preferred embodiment, the step of determining the degree of expression comprises performing an immunoassay with an antibody.

[0150] Selection of biomarkers for decision making Those skilled in the art will be able to readily select an appropriate antibody for use in the present invention. The selected antibody is preferably selective for the antigen of interest, possesses high binding specificity for the antigen, and has minimal cross-reactivity with other antigens. The ability of the antibody to bind to the antigen of interest can be determined by known methods, such as enzyme-linked immunosorbent assay (ELISA), flow cytometry, and immunohistochemistry. Preferably, the antigen of interest bound by the antibody is differentially present in cells or biological samples taken from diseased patients as opposed to cells or biological samples taken from healthy patients. The differential presence of the antigen in different populations can be determined by comparing the binding of the antibody to samples taken from each population of subjects (e.g., a diseased population versus a healthy population). See, e.g., Examples 1-4; also see Figures 1-8. For example, an antigen of interest that is expressed at a higher level in cancer cells than in non-cancerous cells can be determined. See, e.g., Examples 1-4; also see Figures 1-8. Furthermore, the antibody must have relatively high binding specificity for the antigen of interest. The binding specificity of an antibody can be determined by known methods, such as immunoprecipitation, or by in vitro binding assays, such as radioimmunoassay (RIA) or ELISA. Disclosures of methods for selecting antibodies capable of binding an antigen of interest with high binding specificity and minimal cross-reactivity are provided, for example, in U.S. Patent No. 7,288,249, which is incorporated herein by reference in its entirety.

[0151] The present invention provides various methods comprising determining the level of expression of one or more biomarkers described herein. In one embodiment, the method comprises determining the level of expression of any biomarker from any number of Tables 1-14 or 16-23. The biomarkers in Tables 1-14 and 16-23 are generally listed in descending order of level of expression. Biomarkers near the top of these tables generally exhibit greater sensitivity (e.g., detecting lower level differences). The use of such biomarkers can aid in distinguishing between disease states. The biomarkers in Table 15 are listed in ascending order based on the relative standard deviation of fluorescence intensity. Biomarkers near the top of Table 15 are also generally more sensitive due to a lower degree of variation other than that due to the presence of a disease state. In particular, these biomarkers have less overall variability and, therefore, help reduce background noise when comparing levels of expression in diseased individuals, as compared to levels of expression in normal individuals.

[0152]

[0129] Thus, a preferred method comprises determining the degree of expression of biomarkers 1-20 of a particular table, or, in the case of a table containing fewer than 20, all of the listed biomarkers. Alternatively, this method comprises determining the degree of expression of biomarkers 1-10, more preferably biomarkers 1-8, even more preferably biomarkers 1-6, and most preferably biomarkers 1-4, or a subset of biomarkers in any of these groups. In another embodiment, the method comprises determining the degree of expression of any combination of biomarkers from a particular table. In another embodiment, the method comprises determining the degree of expression of any combination of biomarkers from biomarkers numbered 1-20 (or the recited maximum if fewer than 20) of a particular table, preferably any combination of biomarkers from biomarkers numbered 1-10, more preferably any combination of biomarkers from biomarkers numbered 1-8, even more preferably any combination of biomarkers from biomarkers numbered 1-6, and most preferably any combination of biomarkers from biomarkers numbered 1-4, or any subset of biomarkers in any of these groups. In a preferred mode, the method comprises determining the degree of expression of any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of a particular table. Alternatively, the method comprises determining the degree of expression of any particular subset of 4, 5, 6, or 7 biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of a particular table. Alternatively, the method comprises determining the degree of expression of any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15 or 1-20 of a particular table. Of course, one of skill in the art will recognize that it is within the contemplation of the present invention to simultaneously determine the degree of expression of other biomarkers, whether or not associated with the disease of interest.

[0153] Determining the expression levels of multiple biomarkers facilitates the observation of patterns of expression change, and such patterns provide a more sensitive and accurate diagnosis than detection of individual biomarkers. For example, a pattern of change may include multiple specific biomarkers co-expressed at abnormal levels. A pattern of change may further comprise an abnormal elevation of some specific biomarkers, concomitant with an abnormal decrease of other specific biomarkers. One skilled in the art would observe such patterns in the data provided in the figures included herein (see discussion of Example 4 below). Such determinations may be performed in multiplexed or matrix-based formats, such as multiplexed immunoassays.

[0154] In another embodiment, the method comprises determining the degree of expression of any biomarker from at least two tables (e.g., Table 2 and Table 3). In another embodiment, the method comprises determining the degree of expression of biomarkers numbered 1-20 (or, if fewer than 20, the maximum as stated) of a particular table and biomarkers numbered 1-20 (or, if fewer than 20, the maximum as stated) of another table, preferably biomarkers numbered 1-10 from one or both tables, more preferably biomarkers numbered 1-8 from one or both tables, even more preferably biomarkers numbered 1-6 from one or both tables, and most preferably biomarkers numbered 1-4 from one or both tables, or a subset of biomarkers from any of these groups. In another embodiment, the method comprises determining the degree of expression of any combination of a plurality of biomarkers from a particular table and another table. In another embodiment, the method comprises determining the degree of expression of any combination of biomarkers from biomarkers numbered 1-20 (or the stated maximum, if fewer than 20) of a particular table, and any combination of biomarkers from biomarkers numbered 1-20 (or the stated maximum, if fewer than 20) from another table, preferably any combination of biomarkers numbered 1-10 from one or both tables, more preferably any combination of biomarkers numbered 1-8 from one or both tables, even more preferably any combination of biomarkers numbered 1-6 from one or both tables, and most preferably any combination of biomarkers numbered 1-4 from one or both tables, or a subpopulation of biomarkers from any of these groups. In another embodiment, the biomarker(s) from one table is / are not present in any other table.In a preferred mode, the method comprises determining the degree of expression of any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of a particular table and any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of another table. Alternatively, the method comprises determining the degree of expression of any particular subset of four, five, six, or seven biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of a particular table and any particular subset of four, five, six, or seven biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of another table. Alternatively, the method comprises determining the degree of expression of any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15, or 1-20 of a particular table, and any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15, or 1-20 of another table. Of course, one of skill in the art will recognize that it is within the contemplation of the present invention to simultaneously determine the degree of expression of other biomarkers, whether or not associated with the disease of interest.

[0155] It is understood that the same types of combinations are applicable when the method comprises determining the degree of expression of any biomarker from at least three different tables (e.g., Table 2, Table 3, and Table 4). For example, in one embodiment, the method comprises determining the degree of expression of any combination of biomarkers from numbers 1-20 of a first table (or the maximum stated if fewer than 20), any combination of biomarkers from numbers 1-20 of a second table (or the maximum stated if fewer than 20), and any combination of biomarkers from numbers 1-20 of a third table (or the maximum stated if fewer than 20), preferably a combination of biomarkers from biomarkers numbered 1-10 from each table, more preferably a combination of biomarkers from biomarkers numbered 1-8 from each table, even more preferably a combination of biomarkers from biomarkers numbered 1-6 from each table, and most preferably a combination of biomarkers from biomarkers numbered 1-4 from each table. In a preferred mode, the method comprises determining the degree of expression of any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of a first table, any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of a second table, and any particular subset of three biomarkers selected from biomarkers numbered 1-6, 1-8, 1-10, 1-15, or 1-20 of a third table. Alternatively, the method comprises determining the degree of expression of any particular subset of 4, 5, 6, or 7 biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of a first table, any particular subset of 4, 5, 6, or 7 biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of a second table, and any particular subset of 4, 5, 6, or 7 biomarkers selected from biomarkers numbered 1-8, 1-10, 1-15, or 1-20 of a third table.Alternatively, the method comprises determining the degree of expression of any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15 or 1-20 of Table 1, any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15 or 1-20 of Table 2, and any particular subset of 8, 9, 10, 11, 12, or 13 biomarkers selected from biomarkers numbered 1-15 or 1-20 of Table 3. Of course, one of skill in the art will recognize that it is within the contemplation of the present invention to simultaneously determine the degree of expression of other biomarkers, whether or not associated with the disease of interest.

[0156]

[0133] Determining the expression levels of multiple biomarkers facilitates the observation of patterns of expression changes, and such patterns provide a more sensitive and accurate diagnosis than detection of individual biomarkers. This determination can be performed in a multiplexed or matrix-based format, such as a multiplexed immunoassay.

[0157] In other embodiments, no more than 5, 10, 15, 20, 25, 30, 35, or 40 degrees of expression are determined.

[0135] The selection of biomarkers for use in diagnostic or prognostic assays can be facilitated using known relationships between particular biomarkers and their primary interactors. Many, if not all, of the biomarkers identified by the present inventors (see Tables 1-23) are involved in various communication pathways in cells or organisms. Deviations from normal in one component of a communication pathway are expected to be accompanied by related deviations in other members of the communication pathway. Those skilled in the art will appreciate the various databases and available bioinformatics software (e.g., ARIADNE PATHWAY STUDIO, Ariadne, Inc.,<www.ariadne.genomics.com> or ChEMBL Database, European Bioinformatics Institute, European Molecular Biology Laboratory,<www.ebi.ac.uk> (see, for example, ). Diagnostic methods based on determining whether levels of multiple biomarkers are abnormal, where the multiple biomarkers include some biomarkers that are not in the same communication pathway as others in the multiple, may maximize the information gathered by measuring the levels of the biomarkers.

[0158]

[0136] It is further understood that the various combinations of biomarkers discussed above are also applicable to the methods for designing kits and kits described herein.

[0159]

[0137] It will be appreciated that the selection criteria discussed above, including priorities for selecting particular subsets of markers, can be used for any of the methods described herein with respect to these tables accompanying particular methods.

[0160] Physiological characterization methods

[0138] The present invention relates to methods for physiological characterization of individuals in various populations, as described below. As used herein, physiological characterization according to the methods of the present invention includes methods for diagnosing a particular disease, predicting the likelihood that an individual will respond to a therapeutic intervention, monitoring an individual's response to a therapeutic intervention, determining whether an individual is at risk for a particular disease, determining the degree of risk for a particular disease, classifying the degree of disease severity in a patient, and differentiating between diseases that share several symptoms. Generally, these methods rely on determining the degree of expression of specific biomarkers, as described above.

[0161] A. General Population

[0139] The present invention provides methods for physiological characterization of a subject. In one embodiment, the present invention provides a method for physiological characterization of a subject comprising determining the level of expression of at least one biomarker from Table 1A in a physiological sample from the subject, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the level of expression of at least one biomarker from Table 1B, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the level of expression of at least one biomarker from Table 1C, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer.

[0162] In another embodiment, the method comprises determining the degree of expression of SEQ ID NO: 12. In another embodiment, the method comprises determining the degree of expression of SEQ ID NO: 12 and any one of SEQ ID NOs: 1-11 and 13-17.

[0163] In a preferred embodiment, the present invention provides a method for physiological characterization of a subject, comprising determining the degree of expression of a plurality of biomarkers from Table 1A in a physiological sample from the subject, wherein the pattern of expression of the plurality of markers correlates with a change in physiological state or condition, or a disease state (e.g., stage of non-small cell lung cancer) or condition. In another preferred embodiment, the pattern of expression of the plurality of biomarkers from Table 1A is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease, or aids in distinguishing between reactive airway disease and small cell lung cancer. Preferably, the plurality of biomarkers is selected based on a low probability of incorrect pattern classification based on a Student's t-value as calculated in the Examples. In another preferred embodiment, the pattern of expression of the biomarkers from Table 1A correlates with an increased likelihood that the subject has or may have a particular disease or condition. In a further preferred embodiment, the method of determining the degree of expression of a plurality of biomarkers from Table 1A in a subject detects an increased likelihood that the subject will develop, have, or may have a lung disease, such as non-small cell lung cancer or reactive airway disease (e.g., asthma). The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 1A.

[0164]

[0142] The present invention further provides a method of physiological characterization of a subject comprising determining the level of expression of SEQ ID NO: 12 in a physiological sample from the subject, wherein the level of expression of SEQ ID NO: 12 is indicative of the lung disease non-small cell lung cancer or reactive airway disease. In a preferred embodiment, the pattern of expression of a plurality of markers of SEQ ID NO: 12 and any one of SEQ ID NOs: 1-11 and 13-17 is determined and used as described herein.

[0165] In another aspect, the invention provides a method of physiological characterization of a subject, comprising: (a) obtaining a physiological sample from the subject; (b) determining the level of expression of at least one polypeptide selected from the group consisting of SEQ ID NOS:1-17 in the subject; and (c) determining the level of expression of at least one biomarker from Table 1A in the subject, wherein the level of expression of both the polypeptide and the biomarker from Table 1A is indicative of non-small cell lung cancer or reactive airway disease lung disease. In another embodiment, the pattern of expression of a plurality of markers of SEQ ID NOS:1-17 and a plurality of biomarkers from Table 1A is determined and used as described herein.

[0166] In one embodiment, a subject is at risk for lung disease, such as non-small cell lung cancer or reactive airway disease (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At-risk" subjects include individuals who are asymptomatic but have a greater likelihood than the majority of the population of developing the disease due to personal or family medical history, behavior, exposure to disease-causing drugs (e.g., carcinogens), or some other cause. "At-risk" individuals are traditionally identified by aggregating risk factors determined for the individual. The present invention provides improved detection of "at-risk" individuals by determining the degree of expression of associated biomarkers. In one embodiment, the levels of specific disease-associated biomarkers (particularly biomarkers from Table 2 or Table 3) are determined for an individual, and levels that differ from those expected for a normal population indicate the individual is "at risk." In another embodiment, the number of associated biomarkers (from Table 2 or Table 3, as appropriate for the disease) that deviate statistically from normal is determined, with a greater number of aberrant markers indicating a greater risk.

[0167] The embodiments described above refer to the biomarkers in Table 1A. However, it is recognized that in any described embodiment, a biomarker from Table 1B or 1C can be substituted for the biomarker in Table 1A. It is further recognized that the biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0168] B. A group of men

[0146] The present invention provides a method of physiological characterization in a male subject, comprising obtaining a sample from the male subject and determining the degree of expression of at least one biomarker from Table 5A or 16A in the male subject's physiological sample, wherein the degree of expression of the at least one biomarker is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the degree of expression of at least one biomarker from Table 5B or 16B, wherein the degree of expression of the at least one biomarker is indicative of reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the degree of expression of at least one biomarker from Table 5C or 16C, wherein the degree of expression of the at least one biomarker is indicative of reactive airway disease or non-small cell lung cancer.

[0169] In a preferred embodiment, the present invention provides a method of physiological characterization in a male subject, comprising determining the degree of expression of a plurality of biomarkers from Table 5A or 16A in a physiological sample from the male subject, wherein the pattern of expression of the plurality of markers correlates with a physiological state or condition, or a change in a disease state (e.g., stage of non-small cell lung cancer) or condition. In another embodiment, the pattern of expression of a plurality of biomarkers from Table 5A or 16A is indicative of a lung disease such as non-small cell lung cancer or reactive airway disease, or aids in distinguishing between reactive airway disease and small cell lung cancer. Preferably, the plurality of biomarkers is selected based on a low probability of erroneous pattern classification based on a Student's t-value as calculated in the Examples. In another preferred embodiment, the pattern of expression of biomarkers from Table 5A or 16A correlates with an increased likelihood that the male subject has or may have a particular disease or condition. In a further preferred embodiment, the method of determining the degree of expression of a plurality of biomarkers from Table 5A or 16A in a male subject detects an increased likelihood that the male subject will develop, have, or may have a lung disease such as non-small cell lung cancer or reactive airway disease (e.g., asthma). The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 5A or 16A.

[0170] In another aspect, the invention provides a method of physiological characterization of a male subject, comprising: (a) obtaining a physiological sample from the male subject; (b) determining the level of expression of at least one polypeptide selected from the group consisting of SEQ ID NOs:1-17 in the subject; and (c) determining the level of expression of at least one biomarker from Table 5A or 16A in the subject, wherein the level of expression of both the polypeptide and the biomarker from Table 5A or 16A is indicative of non-small cell lung cancer or reactive airway disease lung disease. In another embodiment, the pattern of expression of a plurality of markers of SEQ ID NOs:1-17 and a plurality of biomarkers from Table 5A or 16A is determined and used as described herein.

[0171] In one embodiment, the male subject has non-small cell lung cancer or reactive airway disease (e.g., "At risk" subjects and individuals are at risk for pulmonary diseases (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At risk" subjects and individuals are discussed above. In one embodiment, the levels of specific disease-associated biomarkers (particularly biomarkers from Tables 6, 7, 17, or 18) are determined for a male individual, and levels that differ from those expected for a normal population indicate the individual is "at risk." In another embodiment, the number of relevant biomarkers (from Tables 6, 7, 17, or 18, as appropriate for the disease) that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk.

[0172]

[0150] The above-described embodiments refer to biomarkers in Tables 5A or 16A. However, it is recognized that in any described embodiment, a biomarker from Table 5B or 5C can be substituted for a biomarker from Table 5A, and a biomarker from Table 16B or 16C can be substituted for a biomarker from Table 16A. It is further recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0173] C. A group of women

[0151] The present invention provides a method of physiological characterization in a female subject, comprising obtaining a sample from the female subject and determining the level of expression of at least one biomarker from Table 9A or 20A in the female subject's physiological sample, wherein the level of expression of the at least one biomarker is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the level of expression of at least one biomarker from Table 9B or 20B, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. In another embodiment, the method comprises determining the level of expression of at least one biomarker from Table 9C or 20C, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease or non-small cell lung cancer.

[0174] In a preferred embodiment, the present invention provides a method of physiological characterization in a female subject, comprising determining the degree of expression of a plurality of biomarkers from Table 9A or 20A in a physiological sample from the female subject, wherein the pattern of expression of the plurality of markers correlates with a physiological state or condition, or a change in a disease state (e.g., stage of non-small cell lung cancer) or condition. In another preferred embodiment, the pattern of expression of the plurality of biomarkers from Table 9A or 20A is indicative of a lung disease such as reactive airway disease or non-small cell lung cancer, or aids in distinguishing between reactive airway disease and small cell lung cancer. Preferably, the plurality of biomarkers is selected based on a low probability of erroneous pattern classification based on a Student's t-value as calculated in the Examples. In another preferred embodiment, the pattern of expression of the biomarkers from Table 9A or 20A correlates with an increased likelihood that the female subject has or may have a particular disease or condition. In a further preferred embodiment, the method of determining the degree of expression of a plurality of biomarkers from Table 9A or 20A in a female subject detects an increased likelihood that the female subject will develop, have, or may have a lung disease such as non-small cell lung cancer or reactive airway disease (e.g., asthma). The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 9A or 20A.

[0175] In another aspect, the invention provides a method of physiological characterization of a female subject, comprising: (a) obtaining a physiological sample from the female subject; (b) determining the level of expression of at least one polypeptide selected from the group consisting of SEQ ID NOs:1-17 in the subject; and (c) determining the level of expression of at least one biomarker from Table 9A or 20A in the subject, wherein the level of expression of both the polypeptide and the biomarker from Table 9A or 20A is indicative of non-small cell lung cancer or reactive airway disease lung disease. In another embodiment, the pattern of expression of a plurality of markers of SEQ ID NOs:1-17 and a plurality of biomarkers from Table 9A or 20A is determined and used as described herein.

[0176] In one embodiment, the female subject has non-small cell lung cancer or reactive airway disease (e.g., "At risk" subjects and individuals are at risk for pulmonary diseases (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At risk" subjects and individuals are discussed above. In one embodiment, the levels of specific disease-associated biomarkers (particularly biomarkers from Tables 10, 11, 21, or 22) are determined for a female individual, and levels that differ from those expected for a normal population indicate that the individual is "at risk." In another embodiment, the number of relevant biomarkers (from Tables 10, 11, 21, or 22, as appropriate for the disease) that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk.

[0177] The above-described embodiments refer to biomarkers in Tables 9A or 20A. However, it is recognized that in any described embodiment, a biomarker from Table 9B or 9C can be substituted for a biomarker from Table 9A, and a biomarker from Table 20B or 20C can be substituted for a biomarker from Table 20A. It is further recognized that the multiple biomarkers determined in these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0178] Pulmonary disease The present invention provides various diagnostic and prognostic methods for pulmonary disease. In particular, the present invention provides methods for diagnosing reactive airway disease, and in particular hyper-reactive TH2 and TH 17 The present invention provides methods for diagnosing cell-associated diseases. Reactive airway diseases include asthma, chronic obstructive pulmonary disease, allergic rhinitis, cystic fibrosis, bronchitis, or other diseases manifesting hypersensitivity to various physiological and / or environmental stimuli. In particular, the present invention provides methods for diagnosing asthma and chronic obstructive pulmonary disease, and more particularly, for diagnosing asthma.

[0179]

[0157] The present invention also provides methods for diagnosing non-small cell lung cancer. These methods involve determining the level of expression of at least one biomarker described herein, where the biomarker(s) is indicative of the presence or development of non-small cell lung cancer. For example, the level of expression of the biomarkers described herein can be used to determine the degree of progression of non-small cell lung cancer, the presence of precancerous lesions, or the staging of non-small cell lung cancer.

[0180] In particular embodiments, the subject is selected from individuals exhibiting one or more symptoms of non-small cell lung cancer or reactive airway disease. Symptoms can include cough, shortness of breath, wheezing, chest pain, and hemoptysis; shoulder pain that travels down the outside of the arm, or vocal cord paralysis leading to hoarseness; esophageal involvement can lead to difficulty swallowing. If the central airways are blocked, collapse of a portion of the lung can occur, leading to infection leading to abscess or pneumonia. Metastases to the bone can cause extreme pain. Metastases to the brain can cause symptoms commonly associated with stroke, such as blurred vision, headache, neurological symptoms including epilepsy, or weakness or loss of sensation in parts of the body. Lung cancer often produces symptoms resulting from the production of hormone-like substances by tumor cells. A common paraneoplastic syndrome seen in NSCLC is the production of parathyroid hormone-like substances, which cause elevated calcium in the bloodstream. Asthma typically results in symptoms such as coughing, wheezing, shortness of breath, and chest tightness, pain, or pressure, especially at night. Thus, it is clear that many of the symptoms of asthma are shared with NSCLC.

[0181] Methods for diagnosing reactive airway disease

[0159] The present invention relates to methods for diagnosing reactive airway disease in individuals in various populations, as described below. Generally, these methods rely on determining the degree of expression of particular biomarkers, as described herein.

[0182] A. General Population

[0160] The present invention provides a method for diagnosing reactive airway disease in a subject, comprising: (a) obtaining a physiological sample from the subject; and (b) determining the degree of expression of at least one biomarker from Table 2 in the subject, wherein the degree of expression of the at least one biomarker is indicative of reactive airway disease.

[0183] In a preferred embodiment, the present invention provides a method for diagnosing reactive airway disease in a subject comprising determining the degree of expression of a plurality of biomarkers from Table 2 in a physiological sample from the subject, wherein a pattern of expression of the plurality of markers is indicative of reactive airway disease or correlates with a change in the state of reactive airway disease. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the subject has or may have reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 2. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0184] In one embodiment, the subject is at risk for reactive airway disease. In one embodiment, the levels of certain biomarkers associated with reactive airway disease are determined for the individual, and levels that differ from those expected for a normal population indicate the individual is "at risk." In another embodiment, the number of relevant biomarkers from Table 2 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of reactive airway disease. In another embodiment, the subject is selected from individuals who exhibit one or more symptoms of reactive airway disease.

[0185] In any of the above embodiments, preferred biomarkers for use in this method comprise at least one biomarker from Table 13B. More preferably, all of the biomarkers in this embodiment can be found in Table 13B.

[0186] B. A group of men

[0164] The present invention provides a method for diagnosing reactive airway disease in a male subject, comprising: (a) obtaining a physiological sample from the male subject; and (b) determining the level of expression of at least one biomarker from Table 6 or 17 in the subject, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease.

[0187] In a preferred embodiment, the present invention provides a method for detecting the phenotype of cerebrospinal fluid (C1) of Table 6 or cerebrospinal fluid (C2) in a physiological sample of a male subject. The present invention provides a method for diagnosing reactive airway disease in a male subject, comprising determining the degree of expression of a plurality of biomarkers from Tables 6 or 17, wherein the pattern of expression of the plurality of markers is indicative of reactive airway disease or correlates with a change in the state of reactive airway disease. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the male subject has or may have reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 6 or 17. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0188] In one embodiment, a male subject is at risk for reactive airway disease. In one embodiment, levels of certain biomarkers associated with reactive airway disease are determined for a male individual, and levels that differ from those expected for a normal population indicate the individual is "at risk." In another embodiment, the number of relevant biomarkers from Table 6 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of reactive airway disease. In another embodiment, the male subject is selected from individuals who exhibit one or more symptoms of reactive airway disease.

[0189]

[0167] In another embodiment, the biomarkers for use in this method comprise at least one biomarker from Table 13A. C. A group of women

[0168] The present invention provides a method for diagnosing reactive airway disease in a female subject, comprising: (a) obtaining a physiological sample from the female subject; and (b) determining the level of expression of at least one biomarker from Table 10 or 21 in the subject, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease.

[0190] In a preferred embodiment, the present invention provides a method for diagnosing reactive airway disease in a female subject, comprising determining the degree of expression of a plurality of biomarkers from Table 10 or 21 in a physiological sample from the female subject, wherein a pattern of expression of the plurality of markers is indicative of reactive airway disease or correlates with a change in reactive airway disease status. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the female subject has or may have reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 10 or 21. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0191] In one embodiment, a female subject is at risk for reactive airway disease. In one embodiment, levels of certain biomarkers associated with reactive airway disease are determined for a female individual, and levels that differ from those expected for a normal population indicate that the individual is "at risk." In another embodiment, the number of relevant biomarkers from Tables 10 or 21 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of reactive airway disease. In another embodiment, the female subject is selected from individuals who exhibit one or more symptoms of reactive airway disease.

[0192]

[0171] In another embodiment, the biomarkers for use in this method comprise at least one biomarker from Table 13A. Method for diagnosing non-small cell lung cancer

[0172] The present invention relates to methods for diagnosing non-small cell lung cancer in individuals in various populations, as described below. Generally, these methods rely on determining the degree of expression of particular biomarkers, as described herein.

[0193] A. General Population

[0173] The present invention provides a method for diagnosing non-small cell lung cancer in a subject, comprising: (a) obtaining a physiological sample from the subject; and (b) determining the level of expression of at least one biomarker from Table 2 in the subject, wherein the level of expression of the at least one biomarker is indicative of the presence or development of non-small cell lung cancer.

[0194] In a preferred embodiment, the present invention provides a method for diagnosing non-small cell lung cancer in a subject, comprising determining the degree of expression of a plurality of biomarkers from Table 3 in a physiological sample from the subject, wherein a pattern of expression of the plurality of markers is indicative of non-small cell lung cancer or correlates with a change in the status (i.e., clinical or diagnostic stage) of non-small cell lung cancer. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the subject has or may have non-small cell lung cancer. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 3. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0195] In one embodiment, the subject is at risk for non-small cell lung cancer. In one embodiment, the levels of certain biomarkers associated with non-small cell lung cancer are determined for the individual, and levels that differ from those expected for a normal population indicate the individual is "at risk." In another embodiment, the number of relevant biomarkers from Table 3 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of non-small cell lung cancer. In another embodiment, the subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer.

[0196] In any of the above embodiments, preferred biomarkers for use in this method comprise at least one biomarker from Table 14B. More preferably, all of the biomarkers in this embodiment can be found in Table 14B.

[0197] B. A group of men

[0177] The present invention provides a method for diagnosing non-small cell lung cancer in a male subject, comprising: (a) obtaining a physiological sample from the male subject; and (b) determining the level of expression of at least one biomarker from Table 7 or 18 in the subject, wherein the level of expression of the at least one biomarker is indicative of the presence or development of non-small cell lung cancer.

[0198] In a preferred embodiment, the present invention provides a method for detecting the phenotype of ... The present invention provides a method for diagnosing non-small cell lung cancer in a male subject, comprising determining the degree of expression of a plurality of biomarkers from Tables 7 and 18, wherein the pattern of expression of the plurality of markers is indicative of non-small cell lung cancer or correlates with a change in the state (e.g., stage) of non-small cell lung cancer. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the subject has or may have non-small cell lung cancer. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 7 or 18. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0199] In one embodiment, the male subject is at risk for non-small cell lung cancer. In one embodiment, the levels of certain biomarkers associated with non-small cell lung cancer are determined for a male individual, and levels that differ from those expected for a normal male population indicate that the individual is "at risk." In another embodiment, the number of relevant biomarkers from Table 7 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of non-small cell lung cancer. In another embodiment, the male subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer.

[0200]

[0180] In another embodiment, the biomarkers for use in this method comprise at least one biomarker from Table 14A. C. A group of women

[0181] The present invention provides a method for diagnosing non-small cell lung cancer in a female subject, comprising: (a) obtaining a physiological sample from the female subject; and (b) determining the level of expression of at least one biomarker from Table 11 or 22 in the subject, wherein the level of expression of the at least one biomarker is indicative of the presence or development of non-small cell lung cancer.

[0201] In a preferred embodiment, the present invention provides a method for diagnosing non-small cell lung cancer in a female subject, comprising determining the degree of expression of a plurality of biomarkers from Table 11 or 22 in a physiological sample from the female subject, wherein a pattern of expression of the plurality of markers is indicative of non-small cell lung cancer or correlates with a change in the state (e.g., stage) of non-small cell lung cancer. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the female subject has or may have non-small cell lung cancer. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 11 or 22. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0202] In one embodiment, a female subject is at risk for non-small cell lung cancer. In one embodiment, levels of certain biomarkers associated with non-small cell lung cancer are determined for the female individual, and levels that differ from those expected for a normal female population indicate the individual is "at risk." In another embodiment, the number of relevant biomarkers from Tables 11 or 22 that statistically deviate from normal is determined, with a greater number of aberrant markers indicating a greater risk of non-small cell lung cancer. In another embodiment, the female subject is selected from individuals exhibiting one or more symptoms of non-small cell lung cancer.

[0203]

[0184] In another embodiment, the biomarkers for use in this method comprise at least one biomarker from Table 14A. Methods for differentiating between non-small cell lung cancer and reactive airway disease. The present invention relates to methods for diagnosing lung disease in individuals in various populations, as described below. Generally, these methods rely on determining the degree of expression of specific biomarkers that distinguish between indicators of reactive airway disease and non-small cell lung cancer.

[0204] A. General Population

[0186] The present invention further provides a method of diagnosing lung disease in a subject comprising determining the level of expression of at least one biomarker from Table 4 in said subject, wherein the level of expression of said at least one biomarker from Table 4 aids in distinguishing between reactive airway disease and non-small cell lung cancer. In one embodiment, the subject is diagnosed with reactive airway disease and / or non-small cell lung cancer. For example, the diagnosis can be determined by the level of expression of at least one biomarker in a physiological sample from the subject, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease and / or non-small cell lung cancer.

[0205]

[0187] The present invention further provides a method for diagnosing lung disease in a subject, comprising: (a) obtaining a physiological sample from the subject; and (b) determining the level of expression in the subject of at least one biomarker from Table 4, at least one biomarker from Table 2, and at least one biomarker from Table 3, wherein: (i) the at least one biomarker from each of Tables 2, 3, and 4 are not identical; (ii) the level of expression of at least one biomarker from Tables 2 and 3 is indicative of lung disease as reactive airway disease and non-small cell lung cancer, respectively; and (iii) the level of expression of at least one biomarker from Table 4 aids in distinguishing between the indications of non-small cell lung cancer and reactive airway disease. Preferably, the method includes at least one marker from each table that is not present in any of the other tables.

[0206] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 4, and preferably also a plurality of biomarkers from Table 2, and a plurality of biomarkers from Table 3. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 2, 3, and 4. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0207] In one embodiment, the subject is at risk for non-small cell lung cancer and / or reactive airway disease. In another embodiment, the subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer and / or reactive airway disease.

[0208]

[0190] The present invention further provides a diagnostic method for assisting in distinguishing whether a subject is at risk for or has contracted non-small cell lung cancer or reactive airway disease, comprising: (a) obtaining a physiological sample from a subject at risk for non-small cell lung cancer or reactive airway disease; and (b) determining the degree of expression of at least one biomarker from Table 4 in said subject, wherein the degree of expression of at least one biomarker from Table 4 assists in distinguishing whether said subject is at risk for non-small cell lung cancer or reactive airway disease.

[0209] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 4. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 4. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0210] In one embodiment, the subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to "at risk" subjects are described above, and methods related thereto are contemplated herein.

[0211] B. A group of men

[0193] The present invention further provides a method of diagnosing lung disease in a male subject, comprising determining the level of expression of at least one biomarker from Table 8 or 19 in said subject, wherein the level of expression of said at least one biomarker from Table 8 or 19 aids in distinguishing between reactive airway disease and non-small cell lung cancer. In one embodiment, the male subject is diagnosed with reactive airway disease and / or non-small cell lung cancer. For example, the diagnosis can be determined by the level of expression of at least one biomarker in a physiological sample from the male subject, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease and / or non-small cell lung cancer.

[0212] The present invention further provides a method for diagnosing lung disease in a male subject, comprising: (a) obtaining a physiological sample from the male subject; and (b) determining the level of expression in the subject of at least one biomarker from Table 8, at least one biomarker from Table 6, and at least one biomarker from Table 7, wherein: (i) the at least one biomarker from each of Tables 6, 7, and 8 are not identical; (ii) the level of expression of at least one biomarker from Tables 6 and 7 is indicative of lung disease as reactive airway disease and non-small cell lung cancer, respectively; and (iii) the level of expression of at least one biomarker from Table 8 assists in distinguishing between the indications of non-small cell lung cancer and reactive airway disease. Preferably, the method includes at least one marker from each table that is not present in any of the other tables.

[0213] The present invention further provides a method for diagnosing lung disease in a male subject, comprising: (a) obtaining a physiological sample from the male subject; and (b) determining the level of expression in the subject of at least one biomarker from Table 19, at least one biomarker from Table 18, and at least one biomarker from Table 17, wherein: (i) the at least one biomarker from each of Tables 17, 18, and 19 are not identical; (ii) the level of expression of at least one biomarker from Tables 17 and 18 is indicative of reactive airway disease and non-small cell lung cancer lung disease, respectively; and (iii) the level of expression of at least one biomarker from Table 19 assists in distinguishing between the indicative of non-small cell lung cancer and reactive airway disease. Preferably, the method includes at least one marker from each table that is not present in any of the other tables.

[0214] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 8, and preferably also a plurality of biomarkers from Table 6, and a plurality of biomarkers from Table 7. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the male subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 6, 7, and 8. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0215] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 19, and preferably also a plurality of biomarkers from Table 17, and a plurality of biomarkers from Table 18. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the male subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 17, 18, and 19. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0216] In one embodiment, the male subject is at risk for non-small cell lung cancer and / or reactive airway disease. In another embodiment, the male subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer and / or reactive airway disease.

[0217]

[0199] The present invention further provides a diagnostic method for assisting in distinguishing the likelihood of a male subject being at risk for developing or having non-small cell lung cancer or reactive airway disease, comprising: (a) obtaining a physiological sample from a male subject at risk for non-small cell lung cancer or reactive airway disease; and (b) determining the degree of expression of at least one biomarker from Table 8 or Table 19 in said subject, wherein the degree of expression of at least one biomarker from Table 8 or 19 assists in distinguishing the likelihood that said subject is at risk for non-small cell lung cancer or reactive airway disease.

[0218] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 8. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the male subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 8 or Table 19. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0219] In one embodiment, the male subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to "at risk" subjects are described above, and methods related thereto are contemplated herein.

[0220] B. A group of women

[0202] The present invention further provides a method of diagnosing lung disease in a female subject, comprising determining the level of expression of at least one biomarker from Table 12 or 23 in said subject, wherein the level of expression of said at least one biomarker from Table 12 or 23 aids in distinguishing between reactive airway disease and non-small cell lung cancer. In one embodiment, the female subject is diagnosed with reactive airway disease and / or non-small cell lung cancer. For example, the diagnosis can be determined by the level of expression of at least one biomarker in a physiological sample from the female subject, wherein the level of expression of the at least one biomarker is indicative of reactive airway disease and / or non-small cell lung cancer.

[0221] The present invention further provides a method for diagnosing lung disease in a female subject, comprising: (a) obtaining a physiological sample from the female subject; and (b) determining the level of expression in the subject of at least one biomarker from Table 12, at least one biomarker from Table 10, and at least one biomarker from Table 11, wherein: (i) the at least one biomarker from each of Tables 10, 11, and 12 are not identical; (ii) the level of expression of at least one biomarker from Tables 10 and 11 is indicative of lung disease as reactive airway disease and non-small cell lung cancer, respectively; and (iii) the level of expression of at least one biomarker from Table 12 aids in distinguishing between the indicative of non-small cell lung cancer and reactive airway disease. Preferably, the method includes at least one marker from each table that is not present in any of the other tables.

[0222] The present invention further provides a method for diagnosing lung disease in a female subject, comprising: (a) obtaining a physiological sample from the female subject; and (b) determining the level of expression in the subject of at least one biomarker from Table 23, at least one biomarker from Table 21, and at least one biomarker from Table 22, wherein (i) the at least one biomarker from each of Tables 21, 22, and 23 are not identical; (ii) the level of expression of at least one biomarker from Tables 21 and 22 is indicative of lung disease as reactive airway disease and non-small cell lung cancer, respectively; and (iii) the level of expression of at least one biomarker from Table 23 aids in distinguishing between the indications of non-small cell lung cancer and reactive airway disease. Preferably, the method includes at least one marker from each table that is not present in any of the other tables.

[0223] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 12, and preferably also a plurality of biomarkers from Table 10, and a plurality of biomarkers from Table 11. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the female subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 10, 11, and 12. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0224] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 23, and preferably also a plurality of biomarkers from Table 21, and a plurality of biomarkers from Table 22. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the male subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Tables 21, 22, and 23. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0225] In one embodiment, the female subject is at risk for non-small cell lung cancer and / or reactive airway disease. In another embodiment, the female subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer and / or reactive airway disease.

[0226]

[0208] The present invention further provides a diagnostic method for assisting in distinguishing the likelihood of a female subject being at risk for developing or having non-small cell lung cancer or reactive airway disease, comprising: (a) obtaining a physiological sample from a female subject at risk for non-small cell lung cancer or reactive airway disease; and (b) determining the degree of expression of at least one biomarker from Table 12 or Table 23 in said subject, wherein the degree of expression of at least one biomarker from Table 12 or 23 assists in distinguishing the likelihood that said subject is at risk for non-small cell lung cancer or reactive airway disease.

[0227] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 12 or 23. In another preferred embodiment, the pattern of expression correlates with an increased likelihood that the female subject has non-small cell lung cancer or reactive airway disease. The pattern of expression can be characterized by any technique known in the art for pattern recognition. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 12 or Table 23. Indeed, it will be recognized that the plurality of biomarkers determined by these particular methods can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0228] In one embodiment, the female subject is selected from individuals who exhibit one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to "at risk" subjects are described above, and methods related thereto are contemplated herein.

[0229]

[0211] In any of the methods herein that use biomarkers selected from more than one table, for example for purposes of distinguishing between different disease states or different populations, analysis of results for the biomarkers from individuals can be performed simultaneously or sequentially.

[0230] How to monitor treatment

[0212] The present invention relates to methods for monitoring the treatment of individuals in various populations, as described below. Generally, these methods rely on determining the degree of expression of particular biomarkers.

[0231] A. General Population The present invention further provides a method of monitoring a subject, comprising: (a) first determining in the subject the level of expression of at least one biomarker from Table 1A in a sample obtained from the subject; (b) determining in the subject a second level of expression of at least one biomarker from Table 1A using a second sample obtained from the subject at a time different from the first level of expression; and (d) comparing the first level of expression and the second level of expression. Typically, the subject has undergone a therapeutic intervention between the time the first and second samples are obtained. Detection of a change in the pattern of expression between the first and second determinations can be considered to reflect the effectiveness of the therapeutic intervention. This embodiment is further useful for identifying specific biomarkers that exhibit changes in their level of expression in response to a particular therapeutic intervention.

[0232] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 1A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 1A. Indeed, it is recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0233] The above-described embodiments refer to the biomarkers in Table 1A. However, it is recognized that the biomarkers in Table 1B, Table 1C, Table 2, Table 3, or Table 4 can be substituted for the biomarkers in Table 1A in any described embodiment.

[0234] B. A group of men The present invention further provides a method of monitoring a male subject, comprising: (a) first determining in the male subject the level of expression of at least one biomarker from Table 5A or 16A in a sample obtained from the male subject; (b) determining in the male subject a second level of expression of at least one biomarker from Table 1A or 16A using a second sample obtained from the male subject at a time different from the first level of expression; and (d) comparing the first level of expression and the second level of expression. Typically, the male subject has undergone a therapeutic intervention between the time the first and second samples are obtained. Detection of a change in the pattern of expression between the first and second determinations can be considered to reflect the effectiveness of the therapeutic intervention. This embodiment is further useful for identifying specific biomarkers that exhibit changes in their level of expression in response to a particular therapeutic intervention.

[0235] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 5A or 16A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 5A or 16A. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0236] The above-described embodiments refer to biomarkers in Tables 5A or 16A. However, it is recognized that a biomarker from Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16B, Table 16C, Table 17, Table 18, or Table 19 can be substituted for a biomarker from Table 5A or 16A in any described embodiment.

[0237] C. A group of women The present invention further provides a method of monitoring a female subject, comprising: (a) first determining in the female subject the level of expression of at least one biomarker from Table 9A or 20A in a sample obtained from the female subject; (b) determining in the female subject a second level of expression of at least one biomarker from Table 9A or 20A using a second sample obtained from the female subject at a time different from the first level of expression; and (d) comparing the first level of expression and the second level of expression. Typically, the female subject has undergone a therapeutic intervention between the time the first and second samples are obtained. Detection of a change in the pattern of expression between the first and second determinations can be considered to reflect the effectiveness of the therapeutic intervention. This embodiment is further useful for identifying specific biomarkers that exhibit changes in their level of expression in response to a particular therapeutic intervention.

[0238] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 9A or 20A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 9A or 20A. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0239] The above-described embodiments refer to biomarkers in Tables 9A or 20A. However, it is recognized that biomarkers in Tables 9B, 9C, 10, 11, 12, 20B, 20C, 21, 22, or 23 can be substituted for the biomarkers in Tables 9A or 20A in any described embodiment.

[0240] Method for predicting subject response to therapeutic intervention

[0222] The present invention relates to methods for predicting subject response to therapeutic intervention in various populations, as described below. Generally, these methods rely on determining the degree of expression of particular biomarkers.

[0241] A. General method

[0223] The present invention further provides a method for predicting a subject's response to a therapeutic intervention, comprising: (a) obtaining a physiological sample from the subject; and (b) determining the level of expression of at least one biomarker from Table 1A in the subject, wherein the level of expression of at least one biomarker from Table 1A aids in predicting the subject's response to the therapeutic intervention. Preferred biomarkers for use in this embodiment are biomarkers that indicate responsiveness to the therapeutic intervention of interest by monitoring a population of subjects. This embodiment can also be used to select patients likely to be responsive to treatment.

[0242] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 1A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 1A. Indeed, it is recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0243] The above-described embodiments refer to the biomarkers in Table 1A. However, it is recognized that the biomarkers in Table 1B, Table 1C, Table 2, Table 3, or Table 4 can be substituted for the biomarkers in Table 1A in any described embodiment.

[0244] B. A group of men

[0226] The present invention further provides a method for predicting a male subject's response to a therapeutic intervention, comprising: (a) obtaining a physiological sample from the male subject; and (b) determining the level of expression of at least one biomarker from Table 5A or 16A in the male subject, wherein the level of expression of at least one biomarker from Table 5A or 16A aids in predicting the male subject's response to the therapeutic intervention. Preferred biomarkers for use in this embodiment are biomarkers that indicate responsiveness to a therapeutic intervention of interest by monitoring a population of male subjects. This embodiment can also be used to select male patients likely to be responsive to treatment.

[0245] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 5A or 16A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 5A or 16A. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0246] The above-described embodiments refer to biomarkers in Tables 5A or 16A. However, it is recognized that biomarkers in Tables 5B, 5C, 6, 7, 8, 16B, 16C, 17, 18, or 19 can be substituted for biomarkers in Tables 5A or 16A in any described embodiment.

[0247] C. A group of women

[0229] The present invention further provides a method for predicting a female subject's response to a therapeutic intervention, comprising: (a) obtaining a physiological sample from the female subject; and (b) determining the level of expression of at least one biomarker from Table 9A or 20A in the female subject, wherein the level of expression of at least one biomarker from Table 9A or 20A aids in predicting the female subject's response to the therapeutic intervention. Preferred biomarkers for use in this embodiment are biomarkers that indicate responsiveness to a therapeutic intervention of interest by monitoring a population of female subjects. This embodiment can also be used for selecting female patients likely to be responsive to treatment.

[0248] In a preferred embodiment, the method comprises determining the degree of expression of a plurality of biomarkers from Table 9A or 20A. The plurality of biomarkers can comprise any combination of the biomarkers described above with respect to Table 9A or 20A. Indeed, it will be recognized that the plurality of biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0249] The above-described embodiments refer to biomarkers in Tables 9A or 20A. However, it is recognized that a biomarker from Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20B, Table 20C, Table 21, Table 22, or Table 23 can be substituted for a biomarker from Table 9A or 20A in any described embodiment.

[0250] How to design a kit A. General Population

[0232] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a subject, comprising: (a) selecting at least one biomarker from Table 1A; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0251]

[0233] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a subject, comprising: (a) selecting at least one biomarker from Table 1B; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0252]

[0234] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a subject, comprising: (a) selecting at least one biomarker from Table 1C; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0253]

[0235] The present invention further provides a method for designing a kit for diagnosing reactive airway disease in a subject, comprising: (a) selecting at least one biomarker from Table 2; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0254]

[0236] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer in a subject, comprising: (a) selecting at least one biomarker from Table 3; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0255]

[0237] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a subject, comprising: (a) selecting at least one biomarker from Table 4; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0256]

[0238] In the above method, steps (b) and (c) can alternatively be carried out by (b) selecting a detection agent for detecting said at least one biomarker, and (c) designing a kit comprising said detection agent for detecting said at least one biomarker.

[0257] The present invention further provides methods for designing a kit comprising selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 2 and at least one biomarker from Table 3. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 2, at least one biomarker from Table 3, and at least one biomarker from Table 4. It is understood that these methods further comprise steps (b) and (c) as described above.

[0258]

[0240] It is recognized that the multiple biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0259] B. A group of men

[0241] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a male subject, comprising: (a) selecting at least one biomarker from Table 5A or 16A; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0260]

[0242] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a male subject, comprising: (a) selecting at least one biomarker from Table 5B or 16B; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0261]

[0243] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a male subject, comprising: (a) selecting at least one biomarker from Table 5C or 16C; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0262]

[0244] The present invention further provides a method for designing a kit for diagnosing reactive airway disease in a male subject, comprising: (a) selecting at least one biomarker from Table 6 or 17; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0263]

[0245] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer in a male subject, comprising: (a) selecting at least one biomarker from Table 7 or 18; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0264]

[0246] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a male subject, comprising: (a) selecting at least one biomarker from Table 8 or 19; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0265]

[0247] In the above method, steps (b) and (c) can alternatively be carried out by (b) selecting a detection agent for detecting said at least one biomarker, and (c) designing a kit comprising said detection agent for detecting said at least one biomarker.

[0266] The present invention further provides methods for designing a kit comprising selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 6 and at least one biomarker from Table 7. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 6, at least one biomarker from Table 7, and at least one biomarker from Table 8. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 17 and at least one biomarker from Table 18. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 17, at least one biomarker from Table 18, and at least one biomarker from Table 19. It is understood that these methods further comprise steps (b) and (c), as described above.

[0267]

[0249] It is recognized that the multiple biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0268] C. A group of women

[0250] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a female subject, comprising: (a) selecting at least one biomarker from Table 9A or 20A; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0269]

[0251] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a female subject, comprising: (a) selecting at least one biomarker from Table 9B or 20B; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0270]

[0252] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in a female subject, comprising: (a) selecting at least one biomarker from Table 9C or 20C; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0271]

[0253] The present invention further provides a method for designing a kit for diagnosing reactive airway disease in a female subject, comprising: (a) selecting at least one biomarker from Table 10 or 21; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0272]

[0254] The present invention further provides a method for designing a kit for diagnosing non-small cell lung cancer in a female subject, comprising: (a) selecting at least one biomarker from Table 11 or 22; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0273]

[0255] The present invention further provides a method for designing a kit to aid in diagnosing lung disease in a female subject, comprising: (a) selecting at least one biomarker from Table 12 or 23; (b) selecting a means for determining the degree of expression of said at least one biomarker; and (c) designing a kit comprising a means for determining the degree of expression.

[0274]

[0256] In the above method, steps (b) and (c) can alternatively be carried out by (b) selecting a detection agent for detecting said at least one biomarker, and (c) designing a kit comprising said detection agent for detecting said at least one biomarker.

[0275] The present invention further provides methods for designing a kit comprising selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 10 and at least one biomarker from Table 11. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 10, at least one biomarker from Table 11, and at least one biomarker from Table 12. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 21 and at least one biomarker from Table 22. In another example, the present invention provides a method for designing a kit comprising selecting at least one biomarker from Table 21, at least one biomarker from Table 22, and at least one biomarker from Table 23. It is understood that these methods further comprise steps (b) and (c) as described above.

[0276]

[0258] It is recognized that the multiple biomarkers determined in these particular methods can be selected from a defined table using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination."

[0277] kit

[0259] The present invention provides kits comprising means for determining the degree of expression of at least one biomarker described herein. The present invention further provides kits comprising a detection agent for detecting at least one biomarker described herein.

[0278]

[0260] The present invention provides kits comprising means for determining the degree of expression of at least one biomarker from Table 1A. The present invention provides kits comprising a detection agent for detecting at least one biomarker from Table 1A.

[0279]

[0261] The present invention further provides a kit comprising means for determining the degree of expression of SEQ ID NO: 12. In one embodiment, the kit comprises means for determining the degree of expression of SEQ ID NO: 12 and any combination of SEQ ID NOs: 1-11 and 13-17.

[0280]

[0262] The present invention further provides a kit comprising a detection agent for detecting SEQ ID NO: 12. In one embodiment, the kit comprises a detection agent for detecting SEQ ID NO: 12 and any combination of SEQ ID NOs: 1-11 and 13-17.

[0281]

[0263] The present invention further provides a kit comprising a means for determining the degree of expression of at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17, and a means for determining the degree of expression of at least one biomarker from Table 1A.

[0282]

[0264] The present invention further provides a kit comprising a detection agent for detecting at least one polypeptide selected from the group consisting of SEQ ID NOs: 1-17, and a detection agent for detecting at least one biomarker from Table 1A.

[0283] The above-described embodiments refer to the biomarkers in Table 1A. However, it is recognized that the biomarkers in Table 1B, Table 1C, Table 2, Table 3, Table 4, Table 5A, Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 9A, Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 16A, Table 16B, Table 16C, Table 17, Table 18, Table 19, Table 20A, Table 20B, Table 20C, Table 21, Table 22, or Table 23 can be substituted for the biomarkers in Table 1A in any of the described kits.

[0284]

[0266] The present invention further provides a kit comprising: (a) a first means for determining the degree of expression of at least one biomarker from Table 2; and (b) a second means for determining the degree of expression of at least one biomarker from Table 3, wherein the at least one biomarker from Table 2 and Table 3 are not identical.

[0285]

[0267] The present invention further provides a kit comprising: (a) a detection agent for detecting at least one biomarker from Table 2; and (b) a detection agent for detecting at least one biomarker from Table 3, wherein the at least one biomarker from Table 2 and Table 3 are not identical.

[0286]

[0268] The present invention further provides a kit comprising: (a) a first means for determining the degree of expression of at least one biomarker from Table 2; (b) a second means for determining the degree of expression of at least one biomarker from Table 3; and (c) a third means for determining the degree of expression of at least one biomarker from Table 4, wherein the at least one biomarker from Table 2, Table 3, and Table 4 are not identical.

[0287]

[0269] The present invention further provides a kit comprising: (a) a detection agent for detecting at least one biomarker from Table 2; (b) a detection agent for detecting at least one biomarker from Table 3; and (c) a detection agent for detecting at least one biomarker from Table 4, wherein the at least one biomarker from Table 2, Table 3, and Table 4 are not identical.

[0288] The above-described embodiments refer to biomarkers in Tables 2, 3, and 4. However, it is recognized that biomarkers in Tables 6, 7, 8, 17, 18, or 19, respectively, can be substituted for biomarkers in Tables 2, 3, and 4 in any of the described kits. It is further recognized that biomarkers in Tables 10, 11, 12, 21, 22, or 23, respectively, can be substituted for biomarkers in Tables 2, 3, and 4 in any of the described kits. Still further, those skilled in the art will understand that the present invention contemplates kits comprising means for detecting any combination of the above-described biomarkers for any method requiring the detection of a specific plurality of biomarkers. It is further recognized that the plurality of biomarkers determined in these particular kits can be selected from the defined tables using the criteria discussed above in the section entitled "Selection of Biomarkers for Determination." [Example]

[0289]

[0271] The following examples are provided to illustrate various modes of the invention disclosed herein, but are not intended to limit the invention in any way.

[0290] Example 1 Human blood samples were collected from volunteers. Thirty samples were collected from individuals not known to have either non-small cell lung cancer or asthma. These 30 samples constitute, and are referred to herein as, the "normal population." Twenty-eight blood samples were collected from individuals who had asthma and had been diagnosed as such by a physician. These 28 samples constitute, and are referred to herein as, the "asthma population." Thirty blood samples were collected from individuals known to have, and had been diagnosed as such by a physician. These 30 samples constitute, and are referred to herein as, the "lung cancer population."

[0291] Studies were conducted to select biomarkers whose altered expression levels are believed to be associated with lung cancer or asthma. As used herein, "lung cancer" is meant to include lung cancer known to be non-small cell lung cancer. The following 59 biomarkers were selected for testing: CD40, hepatocyte growth factor ("HGF"), I-TAC ("CXCL11"; "chemokine (C-X-C motif) ligand 11", "interferon-inducible T-cell alpha chemoattractant"), leptin ("LEP"), matrix metalloproteinase ("MMP") 1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP12, MMP13, CD40 soluble ligand ("CD40 ligand"), epithelial cell proliferation, and IL-16. factor (“EFG”), eotaxin (“CCL11”), fractalkine, granulocyte colony-stimulating factor (“G-CSF”), granulocyte-macrophage colony-stimulating factor (“GM-CSF”), interferon gamma (“IFNγ”), interleukin (“IL”) 1α, IL-1β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-15, IL-17, IP-10 , monocyte chemotactic protein 1 (“MCP-1”), macrophage inflammatory protein (“MIP”) 1α, MIP-1β, transforming growth factor α (“TGFα”), tumor necrosis factor α (“TNFα”), vascular endothelial growth factor (“VEGF”), insulin (“Ins”), C-peptide, glucagon-like protein-1 / amylin (“GLP-1 / amylin”), amylin (total), glucagon, adiponectin, plasminogen activator inhibitor 1 (“PAI-1”; The following proteins were selected: (“serpin”) (active / total), resistin (“RETN”; “xcp1”), sFas, soluble Fas ligand (“sFasL”), macrophage migration inhibitory factor (“MIF”), sE-selectin, soluble vascular cell adhesion molecule (“sVCAM”), soluble intercellular adhesion molecule (“sICAM”), myeloperoxidase (“MPO”), C-reactive protein (“CRP”), serum amyloid A (“SAA”; “SAA1”), and serum amyloid P (“SAP”).

[0292]

[0274] Plasma samples from each of the normal, asthma, and lung cancer populations were screened for each of the 59 biomarkers by subjecting the plasma samples to analysis using Luminex's xMAP technology, a quantitative multiplexed immunoassay using automated bead-based technology.

[0293] Several different assay kits were prepared, namely, Millipore's Human Cytokine / Chemokine (Cat. No. MPXHCYTO-60K), Human Endocrinology (Cat. No. HENDO-65K), Human Serum Adipokine (Cat. No. HADKI-61K), Human Sepsis / Apoptosis (Cat. No. HSEP-63K), Human Cardiovascular Panel 1 (Cat. No. HCVD1-67AK), and Human Cardiovascular Panel 2 (HCVD2-67BK); R&D Systems, Inc.'s Human Fluorokine MAP Authentication Base Kit B (Cat. No. LUB00) and Human Fluorokine MAP The MMP Identification Base Kit (catalog number LMP000) was used with Luminex's xMAP technology to screen for biomarkers. Fluorescence intensity levels obtained from the multiplex immunoassay were recorded for each of the 59 biomarkers for each plasma sample in each population. The recorded fluorescence intensity is proportional to the concentration of the corresponding biomarker in the sample and its degree of expression in the individual. The mean, standard deviation, and relative standard deviation for the fluorescence intensity levels associated with each biomarker for each population were calculated. Figures 1A-1C show the mean, standard deviation, and relative standard deviation for each biomarker in the normal (NO), non-small cell lung cancer (LC), and asthma (AST) populations.

[0294]

[0276] Student's t-tests were then used to characterize the differences between disease states for each specific biomarker between each population. The mean fluorescence intensity measurements for each biomarker for samples from normal patients were compared with those for samples from patients with lung cancer and also with those from patients with asthma. Figure 1D shows the differences between the means of the various populations for each marker. Furthermore, the mean fluorescence intensity measurements for patients with lung cancer were compared with those for patients with asthma, and significance was assessed using the Student's t-statistic.

[0295] Further analysis of statistical differences for each biomarker between normal, asthma, and lung cancer populations was performed. To characterize the difference in mean expression levels for each biomarker between populations, Student's t-values were calculated using the t-test function available in the Microsoft Excel software package. The Excel t-test function was used to calculate the probability associated with the Student's t-value under the assumption of equal variances using a two-tailed distribution.

[0296]

[0278] The significance of differences in expression levels between populations was determined by the criterion that any Student's t-value with an associated probability of less than 0.05 is considered significant to indicate the presence of a given condition, whether asthma or lung cancer. Using a criterion of 0.05 or less is generally accepted in the scientific field. Any Student's t-value with an associated probability of greater than 0.1 is considered insignificant to indicate the presence of a given condition. Furthermore, any Student's t-value with an associated probability between 0.051 and 0.1 is determined to be marginal.

[0297]

[0279] Referring now to Figure IE, the Student's t-values with associated probabilities calculated comparing each biomarker for each population are shown. It should be noted that the Student's t-values with associated probabilities shown in Figure IE were calculated based on each of the asthma, normal, and lung cancer populations having a single mean and normal distribution.

[0298] The significance of the differences in biomarker expression levels is used to rank the relative importance of the biomarkers. Biomarkers found to differ most significantly between conditions are classified as relatively more important. Mean fluorescence intensity measurements were examined, and data for all biomarkers with intensities that were not significantly different from the mean intensities of the samples in the other populations were excluded from further analysis. Biomarkers with relatively low relative standard deviations were classified as more significant than those with relatively high standard deviations.

[0299] The direction of deviation, i.e., whether the mean level of a particular marker is increased or decreased in any condition relative to any other condition, was not used to determine the relative significance of a particular marker. In this way, a group of biomarkers was constructed that showed high variability between conditions, a relatively low relative standard deviation, and good instrument detectability (defined as a non-zero uncorrected mean fluorescence intensity). These calculations were used to test the efficiency of the immunoassay and analyzed to determine biomarkers that showed significant differences in expression levels between normal populations and were characteristic of lung cancer and / or asthma conditions and to determine associated reference ranges.

[0300]

[0282] Still referring to Figure 1E, the probability associated with Student's t values was calculated to compare the asthmatic population with the normal population. Significant differences between the asthmatic and normal populations were determined from the Student's t probabilities for the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, MMP-13, CD40 ligand sVCAM-1, HGF, C-peptide, sICAM-1, MMP-7, adiponectin, GM-CSF, and MIF. This determination was made based on the fact that the probability associated with the Student's t value for each of these biomarkers was less than 0.05 when 28 samples from the asthmatic population were compared with 30 samples from the normal population using the Student's t function described herein. The differences between the asthmatic and normal populations for the biomarkers CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, and IL-6 were determined to be not significant because the Student's t probability for each of these biomarkers was significantly greater than 0.05.

[0301]

[0283] As further shown in Figure 1E, probabilities associated with Student's t values were calculated to compare the lung cancer and normal populations. Significant differences between the lung cancer and normal populations were determined from the Student's t probabilities for the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, CD40 ligand, MMP-7, and MMP-12. Again, this determination was based on the Student's t probability being less than 0.05 for each of these biomarkers when comparing 30 samples from the lung cancer population with 30 samples from the normal population, using the Student's t function described herein. Differences between the lung cancer and normal populations for the biomarkers MMP-13, HGF, C-peptide, sICAM, adiponectin, GM-CSF, IL-17, TNFα, ITAC, and MIF were determined to be not significant because the Student's t probability for each of these was significantly greater than 0.05.

[0302]

[0284] The three biomarkers had probabilities associated with Student's t values only slightly greater than 0.05 between the lung cancer and normal populations. Specifically, when comparing the lung cancer population with the normal population, IL-6 had a Student's t probability of 0.076195528, sVCAM-1 had a Student's t probability of 0.08869949, and IL-15 had a Student's t probability of 0.086324372. These biomarkers are considered to have insignificant differences between the lung cancer and normal populations. However, due to the fact that the Student's t probabilities for these three biomarkers are close to 0.05, it is possible that each population may vary significantly between the normal and lung cancer populations.

[0303] Finally, as shown in Figure 1E, further analysis was performed by calculating the probability associated with Student's t values for comparing the lung cancer and asthma populations. Significant differences between the lung cancer and asthma populations were determined from Student's t probabilities for the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, IL-6, MMP-13, sVCAM, HGF, C-peptide, sICAM, adiponectin, GM-CSF, IL-17, IL-15, TNFα, and I-TAC. This determination was based on the Student's t probabilities for each of these biomarkers being less than 0.05 when comparing 30 samples from the lung cancer population with 28 samples from the asthma population, using the Student's t function described herein. Differences between the lung cancer and asthma populations for the biomarkers CD40 ligand, MMP-7, MMP-12, and MIF were determined to be not significant because the Student's t probability for each of these biomarkers was significantly greater than 0.05.

[0304] Example 2 Human blood samples were collected from volunteers. 142 samples were collected from individuals not known to have either non-small cell lung cancer or asthma. These samples constitute, and are referred to as, the "normal population." 108 blood samples were collected from individuals who had asthma and had been diagnosed as such by a physician. These samples constitute, and are referred to herein as, the "asthma population." 146 blood samples were collected from individuals who were known to have, and had been diagnosed as such by a physician. These constitute, and are referred to herein as, the "lung cancer population."

[0305] The same method as described in Example 1 was carried out. Figures 2A-2E show the results obtained. These results provide guidance for selecting suitable biomarkers for the methods of the present invention. In particular, the probabilities for specific markers are useful in this regard.

[0306]

[0288] Figure 2E shows the probability associated with the effectiveness of various biomarkers for discriminating between physiological states in different populations. Probability values of 0.1 or less are highlighted in this table to identify biomarkers of interest. Biomarkers used in preferred methods of the invention are those having probability values of 0.05 or less, more preferably 0.01, and even more preferably 0.001 or less.

[0307] Example 3 Human blood samples were collected from volunteers. 288 samples were collected from individuals not known to have either non-small cell lung cancer or asthma. These samples constitute, and are referred to as, the "normal population." 180 blood samples were collected from individuals known to have asthma and diagnosed as such by a physician. These samples constitute, and are referred to herein as, the "asthma population." 360 blood samples were collected from individuals known to have, and diagnosed as such by a physician. These constitute, and are referred to herein as, the "lung cancer population."

[0308] The same method as described in Example 1 was used. In addition, the Procarta cytokine kit from Panomics (catalog number PC1017) was used. Antibodies against PAI-1 and leptin were used from two separate kits. PAI-1 A and leptin 1 Antibodies against PAI-1 were produced by Millipore. B Antibodies against β-glucan were produced by Panomics. Figures 3A-3E show the results obtained. These results provide guidance for selecting suitable biomarkers for the methods of the present invention. In particular, probability values for specific markers are useful in this regard.

[0309]

[0291] Figure 3E shows the probability associated with the effectiveness of various biomarkers for discriminating between physiological states in different populations. Probability values of 0.1 or less are highlighted in this table to identify biomarkers of interest. Biomarkers used in preferred methods of the invention are those having probability values of 0.05 or less, more preferably 0.01, and even more preferably 0.001 or less.

[0310]

[0292] The resulting data was then separated and analyzed by sex. Figures 4A-4C show the mean fluorescence intensity levels of biomarkers in populations of normal (NO), non-small cell lung cancer (LC), and asthma (AST) women. Figure 4D shows the percent change in the mean of each biomarker in the AST vs. NO, LC vs. NO, and AST vs. LC populations. Figure 4E shows the probability associated with Student's t-values calculated by comparing the mean fluorescence intensity measured for each biomarker, where the means compared are the AST vs. NO, LC vs. NO, and AST vs. LC populations, respectively.

[0311]

[0294] The same information for the male population is shown in Figures 5A-5E. Next, the data for the female and male populations were compared. Figure 6A shows the percent change in the mean of each biomarker in the AST male population compared to the AST female population, the LC male population compared to the LC female population, and the NO male population compared to the NO female population. Figure 6B shows the probability associated with the Student's t-value calculated by comparing the mean fluorescence intensity measured for each biomarker in the male and female populations from Example 3, where the means compared are the AST male and female populations, the LC male and female populations, and the NO male and female populations, respectively.

[0312] Example 4

[0296] The Kruskal-Wallis test is a well-known non-parametric statistical method. The data obtained from Example 3 were separated by sex and analyzed by Kruskal-Wallis (U test). Markers with a probability value of 0.05 or less were considered significant. Markers showing marginal significance (probability between 0.051 and 0.10) and non-significant (probability greater than 0.10) were discarded. The results for the retained markers are shown in Figures 7-8.

[0313] Figure 7A shows the percent change in the mean concentration of each biomarker in the LC vs. NO, AST vs. NO, and AST vs. LC female populations. The scalar sum (i.e., the sum of the absolute percent change for all three comparisons) was also provided and used to rank the biomarkers. Figure 7B shows the probability associated with the Kruskal-Wallis test calculated by comparing the measured concentrations for each biomarker, where the compared populations are the AST vs. NO, LC vs. NO, and AST vs. LC female populations, respectively.

[0314]

[0298] The same information for the male population is shown in Figures 8A and 8B. Biomarkers showed unique gender- and disease-specific patterns. In a gender-independent analysis of LC, 36 markers with absolute changes in cutoff thresholds of at least 25% and 32 markers with cutoffs of at least 50% were identified. In women, 32 markers with cutoffs of at least 25% and 30 with cutoffs of at least 50% were found. In men, 39 markers were found with cutoffs of at least 25% and 37 with cutoffs of at least 50%. The expression of four markers: IL-8 and serum amyloid P (downregulated), ketogenic amyloid A and C-reactive protein (all upregulated), was unique for women with LC compared with NO. Five markers: insulin (downregulated), matrix metalloproteinases-7 and -8, and hepatocyte growth factor (all upregulated) were unique for men with LC compared with NO. Three markers showed inverse expression patterns: (i) VEGF was downregulated relative to NO in women with LC and upregulated in men; (ii) leptin was upregulated in women and downregulated in men, and (iii) MIP-1a was upregulated relative to NO in men with LC and downregulated in women.

[0315] The present invention provides various methods for gender-based identification of disease states. For example, the present invention provides a method of physiological characterization in a male subject comprising determining whether insulin is downregulated and / or matrix metalloproteinases-7 and -8, resistin, and hepatocyte growth factor are upregulated. Such patterns are indicative of disease. Assays within the scope of the present invention include detecting abnormal up / downregulation of three, four, or five of these biomarkers in a male subject.

[0316] In another example, the invention provides a method of physiological characterization in a female subject comprising determining whether IL-8 and / or serum amyloid P are downregulated and / or serum amyloid A and C-reactive protein are upregulated. Such patterns are indicative of disease. Assays within the scope of the invention include detecting abnormal up / downregulation of three, four, or five of these biomarkers in a female subject.

[0317] Example 5 Human blood samples were collected from volunteers. Thirty samples were collected from individuals not known to have either non-small cell lung cancer or asthma. Individuals known not to have either non-small cell lung cancer or asthma constitute, and are referred to as, the "normal population." Twenty-eight blood samples were collected from individuals known to have asthma and diagnosed as such by a physician. Individuals known to have asthma constitute, and are referred to herein as, the "asthma population." Thirty blood samples were collected from individuals known to have non-small cell lung cancer and diagnosed as such by a physician. Individuals known to have non-small cell lung cancer constitute, and are referred to herein as the "lung cancer population." Generally, as used herein, the term "lung cancer" or "lung cancers" is meant to refer to non-small cell lung cancer.

[0318] Eight to ten plasma samples were randomly selected for testing from each of the asthma, normal, and lung cancer populations. Each plasma specimen from each population was exposed to a protease or digestive agent. Trypsin was used as the protease, and its use as a protease is preferred due to its ability to produce highly specific and highly predictable cleavages, due to the fact that trypsin is known to cleave peptide chains at the carboxyl side of lysine and arginine, except when proline is directly following either lysine or arginine. Although trypsin was used, other proteases or digestive agents could be used. It is preferred to use a protease, or mixture of proteases, that cleaves at least as specifically as trypsin.

[0319] Tryptic peptides, the peptides left behind by trypsin after cleavage, were then separated from the insoluble material by centrifugation and subjecting the sample to capillary liquid chromatography with a gradient of aqueous acetonitrile with 0.1% formic acid using a 0.375 x 180 mm Supelcosil ABZ+ column on an Exigent 2D capillary HPLC for chromatographic resolution of the produced tryptic peptides. This separation of peptides is necessary because the electrospray ionization process is subject to ion cosuppression, whereby ions of species with high proton affinities suppress the formation of ions with low proton affinities when they are coeluted from the electrospray emitter, which in this case is at the same end as the HPLC column endpoint.

[0320] This method allows for chromatographic separation of the many peptides produced during trypsin digestion and helps minimize cosuppression problems, thereby maximizing the chance of cosuppression formation of quasi-molecular ions, thereby maximizing ion sampling. The tryptic peptides of each sample were then subjected to LC-ESIMS. LC-ESIMS separated the peptides in each sample in time by passing them through a column in a solvent system consisting of water, acetonitrile, and formic acid, as previously described.

[0321] The peptides were then sprayed with an electrospray ionization source to ionize the peptides and generate peptide pseudomolecular ions as described above. The peptides were passed through a mass analyzer in the LC-MSIMS, where the molecular mass of each peptide pseudomolecular ion was measured. After passing through the LC-MSIMS, a mass spectral readout was generated for each peptide present in the sample from the mass spectral data, i.e., intensity, molecular weight, and time of elution of the peptide from the chromatographic column. The mass spectral readout is generally a graphical representation of the peptide pseudomolecular ion signal recorded by the LC-MSIMS, where the x-axis is the mass-to-charge ratio and the y-axis is the intensity of the pseudomolecular ion signal. These data are then processed by the software system controlling the LC-MSIMS, and the resulting data is acquired and stored.

[0322] After the mass spectral data were obtained and entered into the mass spectral readout, a comparative analysis was performed, in which the mass spectral readout of each plasma specimen tested in the LC-MSIMS for each population was performed both between and within disease states. Mass spectral peaks were compared between each specimen tested in the normal population. Mass spectral peaks were then compared between each specimen tested in the asthma and lung cancer populations. After the intra-condition comparison was performed, an inter-condition comparison was performed, in which the mass spectral readout for each specimen tested in the LC-MSIMS for the asthma population was compared to each specimen tested in the normal population. Similarly, the mass spectral readout for each specimen tested in the LC-MSIMS for the lung cancer population was compared to each specimen tested in the normal population.

[0323] Peptides with mass spectral readouts showing that peptide intensities were discordantly differentially expressed within disease states or did not change substantially (less than a 10-fold variation in intensity) when comparing asthma or lung cancer populations with normal populations were determined to be insignificant and excluded. Generally, the exclusion criteria used involved comparing the peak intensities of at least half of the identified and characterized peptides to a given protein from at least 10 data sets derived from the analysis of individual patient plasma samples from each disease state. If the intensity of the majority of peptide peaks derived from a given protein was at least 10-fold higher than the intensity of 80% of the plasma data sets, the protein was classified as differentially regulated between the two disease state groups.

[0324] However, the identity of the proteins giving rise to the peptides observed to be differentially regulated is unknown and needs to be identified. To perform protein identification, the intensity of the pseudo-molecular ion signal of the peptide was compared across known databases containing libraries of known and suspected proteins and peptides.

[0325] The mass spectral readouts of the tryptic digests for each specimen from each normal, each lung cancer, and each asthma population were input into a known search engine called MASCOT. MASCOT is a search engine known in the art that uses mass spectral data to identify proteins from four major sequence databases: MSDB, NCBInt, SwissProt, and dbEST. These databases contain information on all proteins of known sequence and all putative proteins based on the observation of characteristic protein transcription start regions derived from gene sequences. These databases are continually reviewed for accuracy and redundancy, and are continually added to as new protein and gene sequences are identified and published in the scientific and patent literature.

[0326]

[0311] The search criteria and parameters were input into the MASCOT program, and the mass spectral data from the mass spectral readout for each population was run through the MASCOT program. The mass spectral data entered into the MASCOT program was for all specimens of each pathology. The MASCOT program then ran the mass spectral data for the input peptides against a sequence database by comparing the peak intensity and mass of each peptide with the masses and peak intensities of known peptides and proteins. MASCOT then generated search results, which returned a list of candidate possible protein identification matches, usually known as "significant matches," for each sample analyzed.

[0327] A significant match is determined by the MASCOT program by assigning a score called a "MOWSE score" to each specimen tested. A MOWSE score is a score between -10 and -10. * LOG 10(P), where P is the probability that the observed match is a random event, which correlates to a significant p-value when p is less than 0.05, a standard generally accepted in the scientific field. MOWSE scores of approximately 55 to approximately 66 or greater are generally considered significant. The significance level varies slightly depending on the specific search considerations and database parameters. Significant matches are returned for each peptide run, resulting in a candidate protein list.

[0328]

[0313] A comparative analysis was then performed using the same methods as described in US20090069189, the entire contents of which are incorporated herein by reference. Data from the mass spectral readout were collated with significant matches to confirm the raw data, peak intensity, charge multiplicity, isotope distribution, and adjacent charge state. A reverse search was then performed to add peptides that could be missed by the automated search by the MASCOT program to a candidate list. Additional peptides were identified by selecting the "best match," meaning a protein whose tryptic peptide and its corresponding molecular mass substantially matched the respective parameters of the peptide compared to known amino acids or to a computer digest calculated based on the protein's genetic sequence. These predicted peptide masses were then searched against the raw mass spectral data, and any identified peaks were inspected and qualified as described above. All peptides, including those automatically identified by MASCOT and those identified by manual calculation, were then entered into the mass list used by MASCOT. The refined matches were then used to derive a refined MOWSE score.

[0329] As a result of the identification process, the protein arginase-1 was determined to be significantly differentially expressed between asthma, lung cancer, and / or normal populations. Other proteins identified using this method are BAC04615, Q6NSC8, CAF17350, Q6ZUD4, Q8N7P1, CAC69571, FERM domain containing protein 4, a C2-length splice form of the JCC1445 proteasome endopeptidase complex, syntaxin 11, AAK13083, and AKK130490. See US20090069189, all of which are incorporated herein by reference.

[0330]

[0316] Identifying specific proteins consistently differentially expressed in asthma and lung cancer patients by subjecting proteins in the patient's plasma to trypsin digestion and LC-ESIMS analysis, obtaining mass spectra, and determining whether the mass spectral data contain peaks for one or more of arginase 1, BAC04615, Q6NSC8, CAF17350, Q6ZUD4, Q8N7P1, CAC69571, FERM domain containing protein 4, C2-length splice form of the JCC1445 proteasome endopeptidase complex, syntaxin 11, AAK13083, and AAK130490 makes it possible to diagnose these conditions early in the progression of the disease. The levels of any proteins found in the patient samples were then compared to levels found in a normal population.

[0331] The amino acid sequence disclosed in SEQ ID NO:1 is the first amino acid sequence known for protein BAC04615 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:2 is the first amino acid sequence known for protein Q6NSC8 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:3 is the first amino acid sequence known for protein CAF17350 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:4 is the first amino acid sequence known for protein Q6ZUD4 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:5 is the first amino acid sequence known for protein FERM region containing protein 4 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:6 is the first amino acid sequence known for protein AAK13083 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:7 is the first amino acid sequence known for protein Q8N7P1 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:8 is the first amino acid sequence known for the protein CAC69571 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:9 is the first amino acid sequence known for the C2-length splice form of the protein JCC1445 proteasome endopeptidase complex as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:10 is the first amino acid sequence known for the protein syntaxin 11 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:11 is the first amino acid sequence known for the protein AAK13049 as of the filing date of this application. The amino acid sequence disclosed in SEQ ID NO:12 is the first amino acid sequence known for the protein Arginase-1 as of the filing date of this application.

[0332] Example 6 Selected tissue specimens from asthma patients were subjected to the same method as described in Example 5. See also Application No. 61 / 176,437, the entire contents of which are incorporated herein by reference.

[0333] As a result of the recognition process, the following proteins were determined to be significantly differentially expressed in asthmatic patients:

[0334] [Table 24]

[0335]

[0320] The five specific proteins that are consistently and significantly differentially expressed in asthma patients make it possible to diagnose these conditions early in the progression of the disease by subjecting proteins in a patient tissue sample to trypsin digestion and analysis by LC-ESIMS, obtaining mass spectral data, and determining whether the mass spectral data contains peaks for one or more of SEQ ID NOS: 13-17. The levels of any proteins found in the patient sample are then compared to levels found in a normal population.

[0336] Example 7 Diagnostic Tests for Non-Small Cell Lung Cancer A sample of biological fluid is obtained from a patient for whom diagnostic information is desired. Preferably, the sample is blood serum or plasma. The concentrations of seven of the following 14 biomarkers are determined in the sample: IL-13, I-TAC, MCP-1, MMP-1, MPO, HGF, eotaxin, MMP-9, MMP-7, IP-10, SAA, resistin, IL-5, and sVACM-1. The measured concentration from the sample for each biomarker is compared to the range of concentrations of that marker found in the same fluid in normal human individuals, a population of individuals diagnosed with asthma, and a population of individuals diagnosed with NSCLC. Deviation from the normal range is indicative of lung disease, and deviation from the range for a population of individuals with asthma is indicative of NSCLC. Patient testing using biomarkers from the same 14-species set can be used in a similar manner for the diagnosis of asthma or other reactive airway diseases.

[0337] Example 8 Monitoring treatment for non-small cell lung cancer Pretreatment samples of biological fluids were obtained from patients diagnosed with NSCLC before any treatment for the disease. Preferably, the samples were blood serum or plasma. The concentrations of eight of the following 24 biomarkers were determined in the samples: IL-13, EGF, I-TAC, MMP-1, IL-12(p70), eotaxin, MMP-8, MCP-1, MPO, IP-10, SAA, HGF, MMP-9, MMP-12, amylin (total), MMP-7, IL-6, MIL-1β, adiponectin, IL-10, IL-5, IL-4, SE-selectin, and MIP-1α. The measured concentration for each biomarker from the samples can be compared to the range of concentrations of that marker found in the same fluid in normal human individuals. After the pretreatment sample was obtained, the patient underwent a therapeutic intervention comprising surgery followed by irradiation. The same fluid samples are taken after surgery but before irradiation. Additional samples are taken after each irradiation session. The concentrations of the same eight biomarkers in each sample are determined. Changes in the level of expression of each biomarker are recorded and compared with other symptoms of disease progression.

[0338] Example 9 Selection of predictive biomarkers Pretreatment samples of biological fluids were obtained from a group of patients diagnosed with NSCLC before any treatment for the disease. Preferably, the samples were blood serum or plasma. The concentrations of eight of the following 24 biomarkers were determined in the samples: IL-13, EGF, I-TAC, MMP-1, IL-12(p70), eotaxin, MMP-8, MCP-1, MPO, IP-10, SAA, HGF, MMP-9, MMP-12, amylin (total), MMP-7, IL-6, MIL-1β, adiponectin, IL-10, IL-5, IL-4, SE-selectin, and MIP-1α. The measured concentration for each biomarker from the samples was compared to the range of concentrations of that marker found in the same fluid in normal human individuals. After the pretreatment samples were obtained, each patient underwent a therapeutic intervention comprising surgery followed by irradiation. A sample of the same fluid is taken after surgery, but before irradiation. Additional samples are taken after each irradiation session. The concentrations of 24 biomarkers in each sample are determined. Changes in the level of expression of each biomarker are recorded and compared with other symptoms of disease progression. All markers whose levels change after treatment are identified.

[0339] Example 10 Selection of susceptible patients A biological sample is obtained from a patient diagnosed with NSCLC. Preferably, the sample is blood serum or plasma. The concentration of each biomarker identified in the previous examples in the sample is determined, and patients exhibiting the highest number of biomarkers exhibiting values deviating from normal are selected for treatment.

[0340] Example 11 Diagnostic tests for non-small cell lung cancer in male subjects A sample of biological fluid is obtained from a male patient for whom diagnostic information is desired. Preferably, the sample is blood serum or plasma. The concentrations of seven of the following 14 biomarkers are determined in the sample: I-TAC, MPO, HGF, MMP-1, MMP-8, eotaxin, IL-8, MMP-7, IP-10, sVACM-1, IL-10, adiponectin, SAP, and IFN-γ. The measured concentration from the sample for each biomarker is compared to the range of concentrations of that marker found in the same fluid in normal human male individuals, a population of male individuals diagnosed with asthma, and a population of male individuals diagnosed with NSCLC. Deviation from the normal range is indicative of lung disease, and deviation from the range of the population of individuals with asthma is indicative of NSCLC. Patient testing using biomarkers from the same 14-species set can be used in a similar manner for the diagnosis of asthma or other reactive airway diseases.

[0341] Example 12 Another study for non-small cell lung cancer in male subjects Many, if not all, of the biomarkers identified in Tables 1-15 participate in communication pathways of the type described above. Some biomarkers are associated with each other as primary interactors. Selection of markers for use in diagnostic or prognostic assays can be facilitated using known relationships between particular biomarkers and their primary interactors. Known communication relationships among the biomarkers listed in Table 16B can be seen in Figure 9, generated by the Ariadne system. Figure 9 shows that primary interactors of HGF (hepatocyte growth factor) include sFasL (soluble Fas ligand), PAI-1 (serpin plasminogen activator inhibitor 1) (active / total), Ins (insulin; which further includes C-peptide), EGF (epidermal growth factor), MPO (myeloperoxidase), and MIF (migration inhibitory factor). Other interactors (not primary) include RETN (resistin, xcp1), SAA1 (serum amyloid A, SAA), CCL11 (eotaxin), LEP (leptin), and CXCL11 (chemokine (C-X-C motif) ligand 11, interferon-induced T-cell alpha chemoattractant (I-TAC) or interferon-gamma-inducible protein 9 (IP-9)). Furthermore, Figure 9 shows that two biomarkers, MMP1 and MMP-8 (matrix metalloproteinases 1 and 8), are not on the communication pathway with HGF.

[0342]

[0327] One way to maximize the information gathered by measuring a selection of biomarkers is to do not haveThe goal is to select multiple biomarkers so that the biomarkers are included in the collection. Using the list of biomarkers in Table 16B, if the level of at least HGF or another biomarker that is a primary interactor of HGF and MMP-8 is abnormal in a male subject, the likelihood that the subject has lung cancer appears to be very high. If the level of MMP-1 is also abnormal, the likelihood is even higher. Thus, one method according to the present invention for diagnosing lung cancer in a male subject is to determine the level of at least HGF or another biomarker that is a primary interactor of HGF and MMP-8 and compare this level with the expected range for a normal population to determine whether the levels of these biomarkers are abnormal. In a preferred mode, this diagnostic method further includes determining whether the level of MMP-1 is normal. More preferably, one or more of CXCL11, LEP, SAA1, and / or RETN are also determined and the levels are compared with the expected range for a population of normal individuals. The more of these biomarkers present at abnormal levels, the greater the likelihood that the subject has lung cancer.

[0343] Example 13 Monitoring treatment for non-small cell lung cancer in men Pretreatment samples of biological fluids were obtained from male patients diagnosed with NSCLC before any treatment for the disease. Preferably, the samples were blood serum or plasma. The concentrations of eight of the following 24 biomarkers were determined in the samples: IL-13, I-TAC, EGF, MPO, HGF, MMP-1, MMP-8, MIF, eotaxin, IL-12(p70), MCP-1, MMP-9, SAA, IP-10, amylin (total), MMP-7, resistin, IL-6, MIL-1β, TNF-α, IL-8, IL-5, CRP, and IL-10. The measured concentration for each biomarker from the sample can be compared to the range of concentrations of that marker found in the same fluid in normal human individuals. After the pretreatment sample was obtained, the patient underwent a therapeutic intervention comprising surgery followed by irradiation. The same fluid samples are taken after surgery, but before irradiation. Additional samples are taken after each irradiation session. The concentrations of the same eight biomarkers in each sample are determined. Changes in the level of expression of each biomarker are recorded and compared with other symptoms of disease progression.

[0344] Example 14 Selection of predictive biomarkers Pretreatment samples of biological fluids were obtained from a group of male patients diagnosed with NSCLC before any treatment for the disease. Preferably, the samples were blood serum or plasma. The concentrations of the following 24 biomarkers were determined in the samples: IL-13, I-TAC, EGF, MPO, HGF, MMP-1, MMP-8, MIF, eotaxin, IL-12(p70), MCP-1, MMP-9, SAA, IP-10, amylin (total), MMP-7, resistin, IL-6, MIL-1β, TNF-α, IL-8, IL-5, CRP, and IL-10. The measured concentration for each biomarker from the samples was compared to the range of concentrations of that marker found in the same fluid in normal human individuals. After the pretreatment samples were obtained, each patient underwent a therapeutic intervention comprising surgery followed by irradiation. A sample of the same fluid was obtained after surgery but before irradiation. Additional samples are taken after each irradiation session. The concentrations of the 24 biomarkers in each sample are determined. Changes in the level of expression of each biomarker are recorded and compared with other symptoms of disease progression.

[0345] Example 15 Selection of susceptible patients A sample of biological fluid is obtained from a male patient diagnosed with NSCLC. Preferably, the sample is blood serum or plasma. The concentration of each biomarker identified in the previous examples in the sample is determined, and the patient exhibiting the highest number of biomarkers exhibiting values deviating from normal is selected for treatment.

Claims

[Claim 1] The method described in the specification.

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