WO2014072086A1

WO2014072086A1 - Biomarkers for prognosis of lung cancer

Info

Publication number: WO2014072086A1
Application number: PCT/EP2013/060809
Authority: WO
Inventors: Sam ANSARI; Stephan GEBEL; Ignacio GONZALEZ SUAREZ; Julia HOENG; Florian Martin; Manuel Peitsch; Yang Xiang
Original assignee: Philip Morris Products SA
Current assignee: Philip Morris Products SA
Priority date: 2012-11-09
Filing date: 2013-05-24
Publication date: 2014-05-15
Anticipated expiration: 2015-05-09

Abstract

The present invention relates to panels of biomarkers that can be used to provide a prognosis of an individual's lung cancer and to predict and monitor the outcome of an individual's lung cancer which is or will be under treatment. The invention further provides kits for carrying out said methods.

Description

Biomarkers for prognosis of lung cancer

The present invention relates to biomarkers that can be used to provide a prognosis of an individual's lung cancer and to predict and monitor the outcome of an individual's lung cancer which is or will be under treatment. The invention further provides kits for carrying out said methods.

Lung cancer is one of the most common cancers in the world and is a leading cause of cancer death in men and women in the developed world.

Diagnosis of lung cancer is still mainly based on chest radiograph and computed tomography (CT scan). Bronchoscopy or CT-guided biopsy may be used to obtain further information and to identify the tumor type.

In recent years an increasing amount of reports has appeared on biomarkers that may be used for risk assessment, screening, diagnosis, prognosis, and for selection and monitoring of therapies of lung cancer.

For example, Semmens et al, 2005 (WO 2005/098445) disclose protein biomarkers that are differentially present in the samples of patients with lung cancer and in the samples of control subjects and can thus be used in diagnosing lung cancer. The measurement of these markers, alone or in combination, in patient samples, is reported to provide information that can be correlated with a probable diagnosis of lung cancer or a negative diagnosis (e. g., normal or disease-free). All the markers are characterized by molecular weight.

Gold et al, 201 1 (WO 201 1/03 344) disclose a list of 61 biomarkers that can be used alone or in various combinations to diagnose lung cancer.

YIP et al, 2004 (WO 2004/061410) disclose a method for qualifying lung carcinoma status in a subject, which comprises analyzing a biological sample from said subject for a diagnostic level of a biomarker protein, which is differentially present in samples of a subject with lung cancer and a normal subject that is free of lung cancer. YIP et al, 2004 provide a list of about 50 preferred biomarkers that may be used in such a method.

Sungwhan et al, 2007 (US 2007/0264659) disclose an epigenetic marker for lung cancer. Cigarette smoke is a major risk factor for lung cancer however, there is a subset of patients that develop lung cancers despite no history of smoking. Lung cancer in smokers and non-smokers are biologically different, with the former being usually more aggressive and resistant to conventional therapies, which often results in poor outcome and decreased survival.

The underlying molecular mechanisms promoting lung cancer in smokers are still not well understood. Despite the increasing number of reports on biomarkers for lung cancer detection and classification, there is still a need for identification of biomarkers and biological pathways that are specific lung cancers in smokers, which may best be targeted with different treatments, This need could be met within the scope of the present invention.

The present invention provides biomarkers or a panel of biomarkers that are predictive for smoking-related Sung cancer development and that can be used to develop tools in order to decrease adverse health effects caused by exposure to tobacco smoke and other tobacco-related products. In particular, the present invention provides biomarkers, which were identified based on gene expression patterns that are associated with smoking-related lung cancer development and can be used to provide a prognosis of a cancer or to predict the outcome of a treatment, or both.

The present invention relates to a method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value for the expression of a biomarker; and

ii. determining in a sample taken from a non-smoker suffering from lung cancer and/or from a smoker suffering from lung cancer a reference value for the expression of a biomarker, and

iii. comparing the test values obtained for each biomarker with the reference values obtained from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer,

wherein said biomarker corresponds to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FE 1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally, in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2.

In one embodiment, the present invention thus provides a method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotidecorresponding to at least 2 and up to 1 1 1 different genes depicted in table 1 , table 2, or both table 1 and table 2; and ii. comparing the values obtained for each biomarker in the panel with reference values for the biomarkers from lung cancer of a non-smoker; wherein differences in the values indicate a poor prognosis on the development of the lung cancer in the individual.

In one embodiment, the present invention thus provides a method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 additional genes which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2, and ii. comparing the values obtained for each of the biomarkers used in step (i) with reference values for the same biomarkers from lung cancer of a non- smoker; wherein differences in the va!ues indicate a poor prognosis on the development of the lung cancer in the individual.

In one embodiment, the present invention thus provides a method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2); or 204767_s_at (FEN1 ) and 213523_at (CCNE1); or 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2; and

ii. comparing the values obtained for each biomarker in the panel with reference values for the biomarkers from lung cancer of a non-smoker; wherein differences in the values indicate a poor prognosis on the development of the lung cancer in the individual.

In one embodiment, the present invention thus provides a method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of

i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2; and ii. comparing the values obtained for each biomarker in the panel with reference values for the biomarkers from lung cancer of a non-smoker; wherein differences in the values indicate a poor prognosis on the development of the lung cancer in the individual.

In a specific embodiment, at least one of the biomarker genes is a gene depicted in table 3,

Differential expression may be determined by way of comparison to a control sample, particularly a control sample obtained from an individual, which is a non-smoker and suffers from lung cancer. Alternatively, differential expression can also be determined by comparison with a reference value, wherein the value is obtained from a non- smoker who has lung cancer or from a population of non-smokers who have lung cancer. The difference in gene expression can be represented by the value of fold change or a logarithm of the value of the fold change in base 2 (herein referred to as log fold change).

In various embodiments of the invention, the difference in expression of the at least two, and up to 111 , biomarkers in the test sample is determined to have changed relative to that in the control sample by a log fold change value of more than 0,35, 0.4, 0.5, 0.6, 0.7 0.8, 0.9, 1.0, 1 ,1 , 1 .2, 1 .3, 1.4, 1.5, 1 .6, 1.7, 1.8, 1.9, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, or 5.0.

In various embodiments of the invention, the difference in expression of the at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FE 1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination wit at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2 in the test sample, is determined to have changed relative to that in the control sample by a log fold change value of more than 0.35, 0.4, 0.5, 0.6, 0.7 0.8, 0.9, 1.0, 1 .1 , 1.2, 1.3, 1.4, 1.5, 1 ,6, 1.7, 1.8, 1.9, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, or 5.0.

In certain embodiments of the invention, the sample is obtained from the respiratory system, such as but not limited to lung cells.

In certain embodiments of the invention, the genes representing the biomarkers are differentially expressed in lung adenocarcinomas from smokers when compared to adenocarcinomas from non-smokers and are involved in one or more of the following networks.

a. modulation of the immune response in terms of immune evasion of the tumor;

b. apoptosis and cell survival network; and/or

c. cellular growth and proliferation network.

In one embodiment, the present invention relates to a method of any one of the preceding embodiments, comprising

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of the at least 2 and up to 111 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2;

ii. determining in a biological sample of a control individual a reference value for the expression of each of the at least 2 and up to 11 1 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2; and

iii. comparing the values obtained for each said biomarker genes with the reference value;

wherein said control individual is a non-smoker suffering from lung cancer and wherein differences between the test values and the reference values indicate a prognosis on the outcome of the cancer treatment.

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of the at least 2 of the biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523 it (CCNE1 ), optionally in combination with at least 1 and up to 108, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2;

ii. determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (i); and iii. comparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer and wherein differences between the test values and the reference values indicate a prognosis on the outcome of the cancer treatment.

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2); or 204767_s_at (FEN1 ) and 213523_at (CCNE1); or 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2;

ii, determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); andcomparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer and wherein differences between the test values and the reference values indicate a prognosis on the outcome of the cancer treatment.

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2;

ii. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); andcomparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer and wherein differences between the test values and the reference values indicate a prognosis on the outcome of the cancer treatment.

The difference between or the similarity in the test values and the reference values may be used to forecast or classify the subject into a poor survival group or a good survival group.

The invention further provides a method for predicting or monitoring the outcome of a lung cancer treatment in an individual suffering from lung cancer comprising

i. determining in a biological sample of an individual who suffers from lung cancer a value for the expression of each of the at least 2 and up to 11 1 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2;

ii. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); andand comparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer, wherein at least one of the biomarker genes is a gene depicted in table 3 and wherein differences between the test values and the reference values indicate the probability of said individual's lung cancer sharing one or more treatment outcomes of smoking- related lung cancer.

i. determining in a biological sample of an individual who suffers from lung cancer a value for the expression of each of the at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FE 1 ), 209545_s_at (RIPK2) and 2 3523_at (CCNE1), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2;

ii, determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (i); and comparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer, wherein differences between the test values and the reference values indicate the probability of said individual's lung cancer sharing one or more treatment outcomes of smoking- related lung cancer.

In various embodiments of the invention, the difference between a test value and a reference value which indicates the development of the lung cancer or provides a prognosis is a log fold change value of more than 0.35, 0.4, 0.5, 0.6, 0.7 0.8, 0.9, 1.0, 1.1 , 1 .2, 13, 14, 15, 1 ,6, 1 ,7, 18, 1.9, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, or 4.5.

For a biomarker gene which is up-regulated in lung cancer of smokers (table 1 genes), a positive log fold change value of between 0.35 and 5.0 indicates a poor prognosis on the development of the lung cancer in the tested individual, and vice versa.

Accordingly, for a biomarker gene which is down-regulated in lung cancer of smokers (table 2 genes), a negative log fold change value of between 0.35 and 5.0 also indicates a poor prognosis on the development of the lung cancer in the tested individual, and vice versa.

The log fold change value of each of the biomarker genes in table 1 and 2 can be used as exemplary threshold for each of the biomarker gene.

The samples used in the method according to the invention as described herein may be blood, serum, plasma, sputum, saliva, tissue particularly lung tissue, obtained through biopsy, bronchia brushings, exhaled breath, or urine.

In a specific embodiment of the invention, a biomarker or a panel of biomarkers may be used in the method according to the invention as described herein, which is (a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291_at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1 ), 204767_s_at (FEN1), 213523_at (CCNE1 ), 222036_s_at ( CM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_„x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), 209545_s_at (RIPK2), and 224818_at (SORT1 ).

In a specific embodiment of the invention, a biomarker or a panel may be used in the method according to the invention as described herein of at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 biomarker gene, which is

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2): 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291_at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1), 222036_s_at (MCM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (L1LRB2), 204639_at (ADA), and 224818_at (SORT1 ).

In a specific embodiment of the invention, a biomarker or a panel may be used in the method according to the invention as described herein of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2); or 204767_s_at (FEN1) and 2 3523_at (CCNE1 ); or 209545_s_at (R1PK2) and 213523_at (CCNE1), optionally in combination with at least 1 biomarker gene, which is

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1), 204785_x_at (IFNAR2); 201762_s_ at (PSME2), 204279_at (PS B9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291 _at (IL2RB); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), and 2248 8_at (SORT1 ).

In a specific embodiment of the invention, a biomarker or a panel may be used in the method according to the invention as described herein of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 biomarker gene, which is

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (1L15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291_at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1 ), 222036_s_at (MCM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL12), 209875_s_at (SPP1 ), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1).

In a specific embodiment of the invention, at least one of the biomarker genes in the above identified panel of biomarkers is a gene from table 3.

Fragments of the above identified genes or complementary sequences of said genes, particularly sequences having 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% sequence homology with said gene sequences may also be used within the method according to the invention as described herein in the various embodiments.

The biomarker genes identified in section a) above are up-regulated in lung tumors in smokers and are involved in cell-to-cell signalling and the modulation of the immune response. The overall effect of this group of genes is to promote immune evasion of the tumor.

The biomarker genes identified in section b) above are up-regulated in lung tumors in smokers. The overall effect of these genes is to promote tumor growth by up- regulating processes involved in cell proliferation: DNA replication, cell cycle progression, mitosis, as well the repair of DNA damage.

The biomarker genes identified in section c) above are up-regulated in lung tumors in smokers, with the exception of SORT1 , which is down-regulated. The overall effect is to promote tumor cell survival and protect tumor cells from the activation of apoptosis and other cell death pathways.

In one embodiment of the invention, at least one of the biomarker genes selected from the group of genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) may be used in the method according to the invention as described herein. In particular, at least two of the biomarker genes selected from the group of genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) may be used in the method according to the invention as described herein. In particular, the biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1) may be used in the method according to the invention as described herein. Especially, the above biomarkers may optionally be used in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2.

In a further embodiment of the invention, at least one of the biomarker genes selected from the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) may optionally be used in combination with at least one of the biomarker genes depicted in table 1 , table 2 and/or table 3 in the method according to the invention as described herein. In particular, at least two of the biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) may optionally be used in combination with at least one of the biomarker genes depicted in table 1 , table 2 and/or table 3 in the method according to the invention as described herein. In particular, the three biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1) may optionally be used in combination with at least one of the biomarker genes depicted in table 1 , table 2 and/or table 3 in the method according to the invention as described herein.

In one embodiment, a polynucleotide or a variant thereof may be used in any one of the methods of the invention described herein in the various embodiments, which polynucleotide is complementary to a target gene as depicted in table 1 , table 2, or both table 1 and table 2, and is used as a molecular probe in a hybridization reaction or as a molecular primer in a nucleic acid extension reaction, for the determination of the target and reference value. In a specific embodiment of the invention, at least one of the polynucleotides or a variant thereof is complementary to a biomarker gene from table 3.

In one embodiment, one or more detectably labeled antibodies may be used in any one of the methods of the invention described herein in the various embodiments for the determination of the target and reference value, which antibodies are capable of identifying biomarker gene products encoded by one or more biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, or by conserved variants or peptide fragments thereof. In a specific embodiment of the invention, at least one of the detectably labeled antibodies is capable of identifying a biomarker gene product encoded by one or more biomarker genes depicted in table 3.

In certain embodiments of the invention, determination of biomarker values is accomplished by performing an in-vitro assay, particularly an in-vitro assay selected from the group consisting of an antibody-based assay such as an immunoassay, a histological or cytological assay, an expression level assay such as an RNA expression level assay and an aptamer-based assay.

In certain further embodiments of the invention, the biomarker value may be determined by performing

a. mass spectrometry;

b. an enzyme-linked immunosorbent assay;

c. a microarray-based immunohistochemical analysis; or

d. high-throughput DNA sequencing.

In a specific embodiment of the invention, the biomarker value is determined by performing mass spectrometry.

In another specific embodiment of the invention, an enzyme-linked immunosorbent assay may be performed for determining biomarker values. in another specific embodiment of the invention, a microarray-based immunohistochemical analysis may be performed for determining biomarker values.

In still another specific embodiment of the invention, surface enhanced laser desorption/ionization may be performed for determining biomarker values.

In one aspect of the invention, data analysis is performed within a method according to any one of the preceding embodiments by one or more computer program(s).

In one embodiment, the present invention provides a panel of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or of all 1 11 biomarker genes selected from the group of biomarker genes provided in table 1 , table 2, or both table 1 and table 2, for use in a method according to any one of the preceding embodiments. In a specific embodiment of the invention, at least one of the biomarker genes in this panel is a gene from table 3.

In one embodiment, the present invention provides a panel of (a) at least 2, at least 3, at least 4, at least 5 biomarker genes selected from the group of markers depicted in table 3; (b) and at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 biomarker genes selected from the group of markers depicted in table 1 , table 2, or both table 1 and table 2 for use in a method according to the invention as described in any one of the preceding embodiments.

In a specific embodiment, a panel of at least 10 biomarker genes is provided selected from the group of markers depicted in table 1 , table 2, or both table 1 and table 2 for use in a method according to any one of the preceding embodiments. In a specific embodiment of the invention, at least one of the biomarker genes in this panel is a gene from table 3.

In another specific embodiment of the invention, a panel of at least 50 biomarker genes is provided selected from the group of markers depicted in table 1 , table 2, or both table 1 and table 2 for use in a method according to any one of the preceding embodiments. In a specific embodiment of the invention, at least one of the biomarker genes in this panel is a gene from table 3.

In one embodiment of the invention, the panel comprises a polynucleotide or a variant thereof, which is complementary to a target gene as depicted in table 1 , table 2, or both table 1 and table 2 and can be used as a hybridization probe or a primer. In a specific embodiment of the invention, at least one of the polynucleotides or a variants thereof is complementary to a biomarker gene from table 3.

in one embodiment, the present invention provides a kit for predicting a prognosis on the outcome of cancer treatment in an individual suffering from lung cancer or for predicting or monitoring the outcome of a lung cancer treatment in such an individual, comprising a reagent for detecting differential expression of a panel of at least 2 and up to 50, particularly up to 111 different biomarkers selected from the biomarkers depicted in table 1 , table 2, or both table 1 and table 2. In a specific embodiment of the invention, at least one of the biomarker genes in this panel is a gene from table 3.

In one embodiment, the present invention provides a kit for predicting a prognosis on the outcome of cancer treatment in an individual suffering from lung cancer or for predicting or monitoring the outcome of a lung cancer treatment in such an individual, comprising a device or reagent for detecting differential expression of biomarkes comprising using a composition, in particular a panel of at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEM1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2.

In one embodiment, the present invention provides a kit for predicting a prognosis on the outcome of cancer treatment in an individual suffering from lung cancer or for predicting or monitoring the outcome of a lung cancer treatment in such an individual, comprising a device or reagent for detecting differential expression of a panel of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2); or 204767_s_at (FEN1 ) and 213523_at (CCNE1); or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2.

In one embodiment, the present invention provides a kit for predicting a prognosis on the outcome of cancer treatment in an individual suffering from lung cancer or for predicting or monitoring the outcome of a lung cancer treatment in such an individual, comprising a device or reagent for detecting differential expression of a panel of genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2.

In a specific aspect, the present invention relates to a panel of biomarkers selected from the biomarker genes depicted in table 1 , table 2, or both table 1 and table 2 comprising those biomarker genes from said tables which are identified by GeneBank Accession identifiers and to the use of said biomarkers or panel of biomarkers in a method or a kit according to the invention and described herein in the various embodiments. In a specific embodiment of the invention, at least one of the biomarker genes in this panel is a gene from table 3.

In a further specific aspect, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally further comprising at least 1 and up to 108 different nucleic acid molecules each of which comprises a biomarker polynucleotides corresponding to genes depicted in table 1 , table 2, or both table 1 and table 2 and to the use of said nucleic acid molecules in a method or a kit according to the invention and described herein in the various embodiments.

In a further specific aspect, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2); or 204767_s_at (FEN1) and 213523_at (CCNE1); or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at ieast 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2 and to the use of said biomarkers or a composition, particularly a panel of biomarkers in a method or a kit according to the invention and described herein in the various embodiments.

In a further specific aspect, the present invention relates to a composition, particularly a panel of genes, comprising at Ieast 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) optionally in combination with at Ieast 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2 and to the use of said biomarkers or a composition, particularly a panel of biomarkers in a method or a kit according to the invention and described herein in the various embodiments.

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising comprising genes

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103__at (CCL4), 207072_at (IL18RAP), and 205291_at (1L2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1), 204767_s_at (FEN1 ), 213523__at (CCNE1), 222036_s_at (MCM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), 209545_s_at (RIPK2), and 224818_at (SORT1 ).

In a specific embodiment of the invention, at least one of the biomarker genes in this composition, particularly this panel is a gene from table 3.

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ).

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ) and 209545_s_at (RIPK2).

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 213523_at (CCNE1 ).

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 209545_s_at (RIPK2) and 2 3523_at (CCNE1),

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) optionally in combination with at least one and up to 108 genes, which are different from the above identified genes and which are depicted in table 1 , table 2, or both table 1 and table 2,

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least one and up to 108 genes, which are different from the above identified genes and which are depicted in table 1 , table 2, and/or table 3.

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene of at least 2 of the biomarker genes comprising genes identified by GeneBank Accession numbers 204767_s_at (FEW), 209545_s_at (RIPK2) and 2 3523_at (CCNE1).

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene of at least 2 of the biomarker genes comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least one and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2.

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene of at least 2 of the biomarker genes comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least one and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, and/or table 3.

In certain embodiments, the present invention relates to a composition, particularly a panel of genes, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene of the biomarker genes comprising genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2), or 204767_s_at (FEN1 ) and 213523_at (CCNE1), or 209545_s_at (RIPK2) and 2 3523_at (CCNE1 ), optionally in combination with at least one of the biomarker genes

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291 _at (IL2RB); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL 2), 209875_s_at (SPP1 ), 207697_x_at (LILRB2), 204639_at (ADA) and 224818_at (SORT1 ).

The term "composition" as used herein can be a mixtue of nucleic acid molecules in e.g. a solution or a microarray, wherein the nucleic acid molecule may be immobilized on a substrate. In particular, a composition may be a panel of genes.

Fragments of the above identified genes or complementary sequences of said genes, particularly sequences having 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% sequence homology with said gene sequences, may also be used within the composition, particularly the panel of biomarkers according to the invention as described herein in the various embodiments.

The present invention provides biomarkers or a combination of biomarkers that are predictive for smoking-related lung cancer development and that can be used to develop tools for monitoring adverse health effects caused by exposure to tobacco smoke and other tobacco-related products.

In one aspect of the present invention, biomarkers (genes) were identified based on gene expression patterns that can detect smoking-related !ung cancer development.

In another aspect, the present invention provides biomarkers that can be used to monitor the progress of a treatment or predict the outcome of a treatment, in an individual patient.

The present invention provides a method for predicting or assessing prognosis on the outcome of cancer treatment in an individual suffering from lung cancer, said method, comprising

i. determining in a biological sample of an individual who suffers from lung cancer a value for the expression of each of the at least 2 and up to 111 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2;

ii. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); andd comparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer and wherein differences between the test values and the reference values indicate a prognosis on the outcome of cancer treatment.

The biomarker genes depicted in table 1 are genes which have been found to be up- regulated in lung cancer cells of smokers, whereas the genes in table 2 are down- regulated.

According to the invention, a high level of expression of a biomarker gene of table 1 relative to a reference level determined in a control individual, who is a non-smoker suffering from lung cancer, indicates a poor prognosis for the outcome of cancer treatment and thus a decreased survival duration relative to the control; whereas a lower level of expression of a biomarker gene, which is close to or below the reference level indicates an improved prognosis for the outcome of cancer treatment, which means a survival duration similar to or increased relative the control.

For the biomarker genes of table 2, a poor prognosis correlates with a low level of expression as compared to the reference level and an improved prognosis with an expression level which is similar to or above the reference level.

The reference level of a biomarker can be established from cells from characterized cell lines, or cell samples from a non-smoker who has lung adenocarcinoma or a population of non-smokers who have lung adenocarcinoma.

In one embodiment, the present invention provides a method for monitoring the progress of a lung cancer treatment in an individual, said method comprising determining at suitable time intervals before, during, or after lung cancer therapy, particularly at different time points during the treatment, in a sample taken from said individual differential expression of a composition, particularly a panel of at least 2 and up to 1 1 1 different biomarkers selected from the biomarkers depicted in Table 1 , Table 2, or both Table 1 and Table 2.

in a specific embodiment of the invention, at least one of the biomarker genes in the composition, particularly the panel is a gene from table 3.

In particular, said method comprises

i. determining in a plurality of biological samples a value for the expression of each of the at least 2 and up to 11 1 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, wherein said plurality of biological samples are obtained at a plurality of time points from an individual having lung cancer and receiving a lung cancer therapy;

ii. comparing the values obtained for each said biomarker genes with values of each said biomarker gene obtained at another time point and optionally with reference values for each said biomarker gene of a non-smoker suffering from lung cancer;

wherein differences between the test values obtained at the plurality of time points, or changes in the differences between the test values and the reference values provide a prediction of the outcome of the lung cancer therapy. The method of the invention comprises measuring at suitable time intervals before, during, or after lung cancer therapy, the amount of biomarker gene product. Any change or absence of change in the amount of the biomarker gene product can be identified and correlated with the effect of the treatment on the subject. In a preferred aspect, the method comprises determining the levels of biomarker gene product levels at different time points and to compare these values with a reference level. The observed changes in the differences between the test values and the reference values over time can then be correlated with the disease course, treatment outcome or overall survival.

In one embodiment of the present invention the Matthews Correlation Coefficient (MCC) for the prediction of the smoking status ranges between 0.2 and 1 , particularly between 0.3 and 1 , more particularly between 0.37 and 1.

Detection of the protein biomarkers described herein in a test sample may be performed in a variety of ways well known to those skilled in the art.

In one aspect, the methods of the invention rely on the detection of the presence or absence of biomarker gene expression, or the qualitative or quantitative assessment of either over- or under-expression of biomarker gene in a population of test cells relative to a standard. Such methods utilize reagents such as biomarker polynucleotides and biomarker antibodies as described herein.

In particular, the level of expression of a biomarker gene may be determined by measuring the amount of biomarker messenger RNA, for example, by DNA-DNA hybridization, RNA-DNA hybridization, reverse transcription-polymerase chain reaction (PGR), or real time quantitative PGR; followed by comparing the results to a reference based on samples from clinically-characterized patients and/or cell lines of a known genotype/phenotype. As an alternative to amplification techniques, hybridization assays can be performed.

For example, these techniques find application in microarray-based assays that can be used to detect and quantify the amount of biomarker gene transcript using cDNA- or oligonucleotide-based arrays. icroarray technology allows multiple biomarker gene transcripts and/or samples from different subjects to be analyzed in one reaction. Typically, mRNA isolated from a sample is converted into labeled nucleic acids by reverse transcription and optionally in vitro transcription (cDNAs or cRNAs labeled with, for example, Cy3 or Cy5 dyes) and hybridized in parallel to probes present on an array. See, for example, Schulze et al., Nature Cell Biol., 3 (2001 ), E190; and Klein et al., J Exp Med, 2001 , 1625-1638, which are incorporated herein by reference in their entirety. Standard Northern analyses can be performed if a sufficient quantity of the test cells can be obtained. Utilizing such techniques, quantitative as well as size related differences between biomarker transcripts can also be detected.

High-throughput, massively parallel DNA sequencing techniques, also referred to next generation sequencing techniques, may also be applied to measure the level of expression of a biomarker gene, e.g. sequencing with bridge amplification (lllumina), pyrosequencing (Roche Diagnostics), ligation-based methods, ion torrent technology {Life Sciences) and single-molecule sequencing (Pacific Bio). Many such methods are well known in the art and are automated in commercially available sequencing machines.

The present invention provides isolated biomarker polynucleotides or variants thereof, which can be used, for example, as hybridization probes or primers ("biomarker probes" or "biomarker primers") to detect or amplify nucleic acids encoding a biomarker polypeptide, particularly a biomarker polypeptide encoded by a biomarker gene or polynucleotide selected from the group depicted in table 1 , table 2, or both table 1 and table 2.

Nucleic acid molecules comprising nucleic acid sequences encoding the biomarker polypeptides or proteins of the invention, or genomic nucleic acid sequences from the biomarker genes (e.g., intron sequences, 5' and 3' untranslated sequences), or their complements thereof (i.e. , antisense polynucleotides), are collectively referred to as "biomarker genes", "biomarker polynucleotides" or "biomarker nucleic acid sequences" of the invention. The present invention also provides isolated biomarker polynucleotides or variants thereof, which can be used, for example, as hybridization probes or primers ("biomarker probes" or "biomarker primers") to detect or amplify nucleic acids encoding a polypeptide of the invention. The term "biomarker gene product" thus encompasses both mRNA as well as translated polypeptide as a gene product of a biomarker,

The isolated biomarker polynucleotide according to the invention may comprise flanking sequences (i.e., sequences located at the 5' or 3' ends of the nucleic acid), which naturally flank the nucleic acid sequence in the genomic DNA of the organism from which the nucleic acid is derived. However, an isolated polynucleotide does not include an isolated chromosome, and does not include the poly(A) tail of an mRNA, if present. For example, in various embodiments, the isolated biomarker polynucleotide can comprise less than about 5 kb, 4 kb, 3 kb, 2 kb, 1 kb, 0.5 kb or 0.1 kb of nucleotide sequences which naturally flank the coding sequence in genomic DNA of the cell from which the nucleic acid is derived. In other embodiments, the isolated biomarker polynucleotide is about 10-20, 21-50, 51 -100, 101-200, 201-400, 401-750, 751 -1000, 1001-1500 bases in length.

In various embodiments, the biomarker polynucleotides of the invention are used as molecular probes in hybridization reactions or as molecular primers in nucleic acid extension reactions as described herein. In these instances, the biomarker polynucleotides may be referred to as biomarker probes and biomarker primers, respectively, and the biomarker polynucleotides present in a sample which are to be detected and/or quantified are referred to as biomarker polynucleotides. Two biomarker primers are commonly used in DNA amplification reactions and they are referred to as biomarker forward primer and biomarker reverse primer depending on their 5' to 3' orientation relative to the direction of transcription. A biomarker probe or a biomarker primer is typically an oligonucleotide which binds through complementary base pairing to a subsequence of a biomarker polynucleotide. The biomarker probe may be, for example, a DNA fragment prepared by amplification methods such as by PGR or it may be chemically synthesized. A double stranded fragment may then be obtained, if desired, by annealing the chemically synthesized single strands together under appropriate conditions or by synthesizing the complementary strand using DNA polymerase with an appropriate primer. Where a specific nucleic acid sequence is given, it is understood that the complementary strand is also identified and included as the complementary strand will work equally well in situations where the target is a double stranded nucleic acid. A nucleic acid probe is complementary to a target nucleic acid when it will anneal only to a single desired position on that target nucleic acid under proper annealing conditions which depend, for example, upon a probe's length, base composition, and the number of mismatches and their position on the probe, and must often be determined empirically. Such conditions can be determined by those of skill in the art.

In one aspect of the invention, biomarkers may be detected in the test sample by gene expression profiling. In these methods, mRNA is prepared from a sample and mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the corresponding mRNA. The cDNA may be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT- PCR combined with capillary electrophoresis may be used to measure expression levels of mRNA in a sample. Further details are provided, for example, in "Gene Expression Profiling: Methods and Protocols", Richard A. Shimkets, editor, Humana Press, 2004 and US patent application 20100070191.

In another aspect of the invention, detection of the biomarkers described herein may also be accomplished by an immunoassay procedure. The immunoassay typically includes contacting a test sample with an antibody that specifically binds to or otherwise recognizes a biomarker, and detecting the presence of the antibody bio marker complex in the sample. The immunoassay procedure may be selected from a wide variety of immunoassay procedures known to those skilled in the art such as, for example, competitive or non-competitive enzyme-based immunoassays, enzyme-linked immunosorbent assays (ELISA), radioimmunoassay (RIA), and Western blots, etc. Further, multiplex assays may be used, including antibody arrays, wherein several desired antibodies are placed on a support, such as a glass bead or plate, and reacted or otherwise contacted with the test sample.

Antibodies used in these assays may be monoclonal or polyclonal, and may be of any type such as IgG, IgM, IgA, IgD and IgE. Monoclonal antibodies may be used to bind to a specific epitope offered by the biomarker molecule, and therefore may provide a more specific and accurate result. Antibodies may be produced by immunizing animals such as rats, mice, and rabbits. The antigen used for immunization may be isolated from the samples or synthesized by recombinant protein technology. Methods of producing antibodies and of performing antibody- based assays are well-known to the skilled artisan and are described, for example, more thoroughly in Antibodies: A Laboratory Manual (1988) by Harlow & Lane; Immunoassays: A Practical Approach, Oxford University Press, Gosling, J. P. (ed.) (2001) and/or Current Protocols in Molecular Biology (Ausubel et al.) which is regularly and periodically updated. In certain embodiments, the present invention also provides "biomarker antibodies" including polyclonal, monoclonal, or recombinant antibodies, and fragments and variants thereof, that immunospecifically binds the respective biomarker proteins or polypeptides encoded by the genes or cDNAs (including polypeptides encoded by mRNA splice variants) as listed in tables 1 and 2.

Various chemical or biochemical derivatives of the antibodies or antibody fragments of the present invention can be produced using known methods. One type of derivative which is diagnostically useful as an immunoconjugate comprising an antibody molecule, or an antigen-binding fragment thereof, to which is conjugated a detectable label. However, in many embodiments, the biomarker antibody is not labeled but in the course of an assay, it becomes indirectly labeled by binding to or being bound by another molecule that is labeled. The invention encompasses molecular complexes comprising a biomarker antibody and a label, as well as immunocomplexes comprising a biomarker polypeptide, a biomarker antibody, and immunocomplexes comprising a biomarker polypeptide, a biomarker antibody, and a label.

Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, β-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferones, fluoresceins, fluorescein isothiocyanate, rhodamtnes, dichlorotriazinylamine fluorescein, dansyl chloride, phycoerythrins, Alexa Fluor 647, Alexa Fluor 680, DilC_ig(3), Rhodamine Red-X, Alexa Fluor 660, Alexa Fluor 546, Texas Red, YOYO-1 + DNA, tetramethylrhodamine, Alexa Fluor 594, BODIPY FL, Alexa Fluor 488, Fluorescein, BODIPY TR, BODIPY T R, carboxy SNARF-1 , FM 1-43, Fura-2, Indo- 1 , Cascade Blue, NBD, DAPI, Alexa Fluor 350, aminomethylcoumarin, Lucifer yellow, Propidium iodide, or dansylamide; an example of a luminescent material includes luminol; examples of bioluminescent materials include green fluorescent proteins, modified green fluorescent proteins, luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125| 1311 35g _or 3|— [ Immunoassays for biomarker polypeptides will typically comprise incubating a sample, such as a biological fluid, a tissue extract, freshly harvested cells, or lysates of cells, in the presence of a detectably labeled antibody capable of identifying biomarker gene products or conserved variants or peptide fragments thereof, and detecting the bound antibody by any of a number of techniques well-known in the art. One way of measuring the level of biomarker polypeptide with a specific biomarker antibody of the present invention is by enzyme immunoassay (EIA) such as an enzyme-linked immunosorbent assay (ELISA) (Voder, A. et a/., J. Clin. Pathol. 31:507-520 (1978); Butler, J.E., Meth. Enzymol. 73:482-523 (1981 ); Maggio, E. (ed.), Enzyme Immunoassay, CRC Press, Boca Raton, FL, 1980). The enzyme, either conjugated to the antibody or to a binding partner for the antibody, when later exposed to an appropriate substrate, will react with the substrate in such a manner as to produce a chemical moiety which can be detected, for example, by spectrophotometric, or fluorimetric means.

The biological sample may be brought in contact with and immobilized onto a solid phase support or carrier such as nitrocellulose, or other solid support which is capable of immobilizing cells, cell particles or soluble proteins. The support may then be washed with suitable buffers followed by treatment with the detectably labeled biomarker antibody. The solid phase support may then be washed with the buffer a second time to remove unbound antibody. The amount of bound label on solid support may then be detected by conventional means. A well known example of such a technique is Western blotting.

In various embodiments, the present invention provides compositions comprising labeled biomarker polynucleotides, or labeled biomarker antibodies to the biomarker proteins or polypeptides or labeled biomarker polynucleotides and labeled biomarker antibodies to the biomarker proteins or polypeptides according to the invention as described herein. in addition to antibody-based techniques, the biomarkers described herein may also be detected and quantified by mass spectrometry. Mass spectrometry is a method that employs a mass spectrometer to detect ionized protein markers or ionized peptides as digested from the protein markers by measuring the mass-to-charge ratio (m/z). Labeling of biomarkers (along with other proteins) with stable heavy isotopes (deuterium, carbon-13, nitrogen-15, and oxygen- 18) can be used in quantitative proteomics. These are either incorporated metabolically in sample cells cultured briefly in vitro, or directly in samples by chemical or enzymatic reactions, Light, and heavy labeled biomarker peptide ions segregate and their intensity values are used for quantification. For example, analytes may be introduced into an inlet system of the mass spectrometer and ionized in an ionization source, such as a laser, fast atom bombardment, plasma or other suitable ionization sources known to the art. The generated ions are typically collected by an ion optic assembly and introduced into mass analyzers for mass separation before their masses are measured by a detector. The detector then translates information obtained from the detected ions into mass-to-charge ratios.

The invention also provides compositions comprising biomarker polynucleotides, biomarker polypeptides, or biomarker antibodies according to the invention as described herein in the various embodiments. The invention further provides diagnostic reagents for use in the methods of the invention, such as but not limited to reagents for flow cytometry and/or immunoassays that comprise fluorochrome- labeled antibodies that bind to one of the biomarker polypeptides of the invention as described herein.

In one embodiment, the invention provides diagnostic reagents that comprise one or more biomarker probes, or one or more biomarker primers. A diagnostic reagent may comprise biomarker probes and/or biomarker primers from the same biomarker gene or from multiple biomarker genes. In another embodiment, the invention also provides diagnostic compositions that comprise one or more biomarker probes and biomarker polynucleotides, or one or more biomarker primers and target polynucleotides, or biomarker primers, biomarker probes and biomarker target polynucleotides.

In some embodiments, the diagnostic compositions comprise biomarker probes and/or biomarker primers and a sample suspected to comprise biomarker target polynucleotides. Such diagnostic compositions comprise biomarker probes and/or biomarker primers and the nucleic acid molecules (including RNA, mRNA, cRNA, cDNA, and/or genomic DNA) of a subject in need of a diagnosis/prognosis of lung cancer.

The present invention also provides kits for practicing the methods of the invention. The kits can be used for clinical diagnosis and/or laboratory research. In one embodiment, a kit comprises one or more diagnostic reagents in one or more containers. Preferably, the kit also comprises instructions in any tangible medium on the use of the diagnostic reagent(s) in one or more methods of the invention.

For nucleic acid-based methods, such as hybridization assays or polymerase chain reaction, a diagnostic reagent in the kit may comprise at least one biomarker polynucleotide, biomarker probe, and/or biomarker primer based on the biomarkers depicted in table 1 , table 2, or both table 1 and table 2. The diagnostic reagents may be labeled, for example, by one or more different fluorochromes. Such a kit may optionally provide in separate containers enzymes and/or buffers for reverse transcription, in vitro transcription, and/or DNA polymerization, nucleotides, and/or labeled nucleotides, including fluorochrome-labeled nucleotides. Also included in the kit may be positive and negative controls for the methods of the invention.

For protein-based methods, such as immunoassays, a diagnostic reagent in the kit may comprise a biomarker antibody, which may be labeled, for example, by a ftuorochrome. Such a kit may optionally provide in separate containers buffers, secondary antibodies, signal generating accessory molecules, labeled secondary antibodies, including fluorochrome-labeled secondary antibodies. The kit may also include unlabeled or labeled antibodies to various cell surface antigens which can used for identification or sorting of subpopulations of cells, Also included in the kit may be positive and negative controls for the methods of the invention. The positive and/or negative controls included in a kit can be nucleic acids, polypeptides, cell lysate, cell extract, whole cells from patients, or whole cells from cell lines.

The classification of the subject into the smoker-like group relates to the biomarker signature determined for the subject. The classification into smoker-like signature may be independent of the smoking status, that means also non-smoking subjects may be comprised, which reveal a biomarker gene signature, which is usually a typical smoker tumor gene signature as described herein. If a subject is classified into the smoker-like signature group according to the present invention, the subject has a poor survival prognosis.

Brief Description of the Figures

Figure 1 : Interaction term. (A) Change in expression of this gene is due only to cigarette smoke. (B) Change in expression of this gene is specifically related to smoking-related lung cancers. Y-axis represents expression level of the gene. Figure 2: Apoptosis and cell survival network that characterizes cancers from smokers. The numbers below the biomarkers are the log value of the fold change and the p value.Solid lines indicate a direct interaction between the biomarkers; dotted lines indicate an indirect interaction; loops indicate self regulation.

Figure 3: Volcano plot for the interaction model. The coefficients of the interaction model, gene= ₀+ ptSmoking + p₂ ^*Tissue+ ₃ ^*TissuexSmoking+e, are estimated using LIMMA. Figure 3A - Volcano plot for the coefficients associated with the smoking effect (βι), B) Volcano plot fort he coefficients associated with the tumor tissue effect (β₂) andC) Volcano plot for coefficients associated with the interaction effect (β₂). The genes that show significant interaction effect were selected for subsequent analysis as they capture the differences in gene expression between healthy and tumor tissue in smokers and non-smokers.

Figure 4: Biological processes and canonical functions associated with lung adenocarcinoma in smokers using Ingenuity Pathway Analysis. The top ten biological functions (A) and canonical pathways (B) are grouped based on p values (Fisher exact test). The threshold is less than 0.05.

Figure 5: Immune response and cell-to-cell signaling network. The molecules in the network represent genes up regulated in lung tumors from smokers when compared to both healthy tissue and tumors from non-smokers. Grey-shaded sections indicate groups of molecules participating in the processes indicated. Color intensity is a qualitative representation of fold-change. Straight lines indicate direct interaction. Dashed lines indicate indirect interactions.

Figure 6: Cell proliferation (A) and cell survival (B) subnetworks. The molecules in the network represent genes dysregulated in lung tumors from smokers when compared to both healthy tissue and tumors from non-smokers. Grey color indicates upregulated genes. Except from SORT1 , which is a down regulated gene. White color indicates no significant differences in fold change. Color intensity is a qualitative representation of fold-change. Straight lines indicate direct interaction. Dashed lines indicate indirect interactions.

Figure 7: Interactions between all networks. Note how the upstream regulators IFNG, TNF and IL1 B are heavily interacting with multiple components of the networks. Molecules represent genes dysregulated in lung tumors from smokers when compared to both healthy tissue and tumors from non-smokers. Grey color indicates upregulated genes. Except from HEXB, CAT and SORT1 , which are downregulated genes. White color indicates no significant differences in fold change. Color intensity is a qualitative representation of fold-change. Straight lines indicate direct interaction. Dashed lines indicate indirect interactions.

Figure 8: Heatmap of gene signature in training data. The three genes identified for deriving a predictive signature shows a differentiation between smokers and non- smokers in the tumors. These three genes have a higher average expression in smokers as compared to non-smokers. Furthermore this difference is specific to the tumour samples as those genes have a significant interaction effect. Grey and black colors in the side bar denote non-smokers and smokers respectively. Grey scales in the heatmap denote positive (unfilled) and negative (dashed lines) Z-scores respectively. Grey scale color intensity is a qualitative representation of fold-change.

EXAMPLES

The foregoing description will be more fully understood with reference to the following Examples. The Examples are, however, exemplary methods of practicing the present invention and are not intended to limit the scope of the invention. The present invention is based on a study to identify those genes that are affected differentially in tumor tissue and healthy tissue due to exposure to cigarette smoke.

1. Study design and subject selection

Tumor and normal lung tissue samples were obtained from 120 subjects, including smokers (n=60) and non-smokers (n=60) from Indivumed Biobank (Indivumed

GmbH, Hamburg, Germany), Non-smokers are defined as either subjects that had never smoked during life or those that had stopped smoking for at least 16 years before samples were obtained. The rationale behind the incorporation of former smokers that have not smoked for 16 years is that the risk of developing lung cancer for this group is comparable to the risk of a person that has never smoked. Subject and sample selection was strictly based on the study purpose and the availability of tissue samples according to the experimental requirements, demographic and clinical data. The group composition was analyzed with Statisfica software (Statsoft, Tulsa, US) which resulted in overall homogenous subject population. Ethical guidelines and confidentiality have been strictly assured and subjects gave written consent to participate in the study. Normal Sung tissue was obtained from smokers (n=30) and non-smokers (n=30) treated from non-cancerous Sung diseases such as hamartoma, chronic pneumonia, granuloma or normal lung tissue from patients with lung metastasis. Lung tumor tissue was obtained from smokers (n=30) and non-smokers (n=30) with lung cancer histologically classified as adenocarcinoma of the lung. None of the adenocarcinoma patients had a metastasis.

2. Tissue collection and preparation

Tissue collection and preparation was performed at Indivumed Biobank. Freshly frozen lung tissue samples from the subject population were analyzed for normal and tumor cell content by staining before further sectioning. All tissues were collected under standardized conditions, snap frozen and stored in liquid nitrogen. The ischemia times are in alt cases below 15 minutes. Each tissue block was quality controlled and only tissues with low amount of necrotic and / or apoptotic areas (<10%) as well as a tumor content of at least 40-50% were used for the study.

For the analysis, 5μΜ sections were cut in a cryostat and H&E stained. Stained sections were analyzed by microscopy. 16-32 x 20μΜ sections, depending on tissue size, were cut and re-suspended in 500μΙ RNA later solution (Qiagen) with RNAse inhibitor (Applied Biosystems) to assure RNA integrity and stored at -80°C until extraction.

3_ RNA extraction

Frozen tissue sections stored in RNA later / RNAse inhibitor solution were homogenized in RLT lysis buffer (Qiagen). RNA samples were DNAse-treated and extracted with Qiagen RNeasy kit following manufacturer's instructions. The quality of the prepared RNA was documented by microcapillary electrophoresis using an Agilent 2100 Bioanalyzer (Agilent Technologies). Only those RNA samples with a RIN number 7.0 were selected for hybridization.

4, cDNA synthesis and amplification cDNA and cRNA were synthetized using the One Cycle cDNA kit (Affymetrix) and following manufacturer's instructions. Unincorporated nucleotides were removed by using Qiagen RNeasy purification columns (Qiagen). The RNA yield was determined by measuring the absorption at 260nm. 5. Fragmentation, hybridization, detection

Fragmentation of the cRNA was performed in 2X fragmentation buffer (Affymetrix kit component). The fragmented cRNA was examined with an Agilent 2100 Btoanalyzer (Agilent Technologies) using non-fragmented cRNA as a control. Each fragmented cRNA sample was hybridized onto Affymetrix HG U133 plus 2.0 chips together with hybridization controls, Hybridization, washing and detection were performed using an Affymetrix fluid station. Staining for detection was performed using a Streptavidin- phycoerythrin solution and scanned using an Affymetrix GeneChip scanner following manufacturer's instructions.

6. Identification of Biomarker Genes

The expression values of a total of 54,613 probe sets were available in the CEL files generated by measurements of expression levels obtained from Affymetrix gene chips. Removing constant probe sets and the probesets for which the 95%-quantile does not exceed 7 in log2-scale, 19,078 probesets remained (i.e., probesests for which at least 5% of the sample values are above 7.). The remaining probe sets were subsequently mapped to gene symbols. For probeset on the gene chip, it is associated with one gene symbol but for a given gene symbol, several probesets may correspond to it. To choose a representative probeset for a given gene symbol, the interaction term and its p-value is calculated. Among the probesets corresponding to a given gene symbol, the one with the lowest p-value is used.. After this process, 1 Q'966 genes remained and were used for further analysis. The estimation for each gene was performed using the R-package limma. As can be seen in Figure 3, many genes show a significant differential effect due both to the overall smoking effect and tissue difference.

An interaction model was used to estimate the effect of smoke exposure and tissue response as well as the effects of their interactions. An interaction effect is a change in the simple main effect of one variable over levels of the second variable. In this analysts, positive interaction effect means that the effect of smoke exposure in lung is larger in tumor tissue than in healthy tissue. The objective here is to detect a change between tumor and healthy tissues that are different in smokers as compared to the change between tumor and healthy tissues in non-smokers. So the linear regression model used for each gene is : Log2expression value = bO+b1^*SmokingStatus+b2^*Tissue+b3 ^* Tissue:SmokingStatus+ epsilon.

The gene expression values (log2-scale) are modeled as an intercept (bO) plus a smoking status effect (b1) plus a tissue effect (b2) plus an interaction term effect plus a residual term (b3).

The regression coefficients are estimated by the method of ordinary least squares which minimizes the sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the linear approximation, i.e., minimizing [jgene -( b0+b1 ^*SmokingStatus+b2^*Tissue+b3 ^*

Tissue:SmokingStatus) ||^Λ2

For each coefficient, it is tested if it is non-zero H0:bi=0. It is computed under HO bi/sd(bi) which will follow a t-distribution (Student). The significance is assessed by moderated t-statistics [Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1 , Article 3]. In all cases, we used as cutoff values a log ratio > 0.35 and p <0.05.

The results of the statistical analyses identified a total of 1 11 genes (including the three high priority markers identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 )) that are specifically up regulated (88 genes) or down regulated (23 genes) in lung adenocarcinomas from smokers when compared to the lung adenocarcinoma of non-smokers.

7. Prediction of smoking status on an independent dataset.

Forward linear discriminant analysis (FLDA) was applied to the dataset in order to identify a sparse gene signature and build a classifier, which was then utilized to predict the smoking status of samples in an independent data set from Landi et al. (GSE10072).

The FLDA takes as input thejnolecular profile, typically gene expression levels, as well as the phenotype of interest. In our study the phenotype of interest is the smoking status. Given a gene expression profile matrix X with m samples and n genes and the phenotype vector, a predictive signature is derived from the following steps. Firstly, a moderated t-test for the two phenotypes (smoking and non-smoking) is performed for each gene (using the R-package limma). The genes are reordered according to the decreasing order of the absolute value of the statistics t; (2) Secondly, for all j, the j genes with the highest t-statistlc, in absolute value, are selected for training a classifier. Here, a Linear Discriminant Analysis (LDA) was used. The average of the Matthews correlation coefficient (MCC) obtained in a cross validation experiment (five times, five-fold) is recorded. The process above is repeated for all j's. Lastly, the index for which the average MCC is maximum will serve to select the genes that will constitute the gene signature The final prediction model is the trained on this set of genes with LDA.

Formula for the calculation of the MCC value: TP x TN - FP x FN

.J(TP + FN )(TN + FP)(TP + FP ){TN + FN) ^'

Despite the fact that both datasets were generated in different Affymetrix platforms (Human Genome U133 Plus 2.0 Array in the present study vs Human Genome U133A Array in the Landi dataset), which makes it harder to predict the smoking status of samples, FLDA identified a gene signature composed of three genes: FEN1 , RIPK2, and CCNE1 which are strongly involved in the regulation of cell proliferation and survival. An LDA model is trained on data of the present study. The heatmap of this gene signature in the training data is shown in Figure 8. By applying this model, making sure that the means for each gene are equal (centering both datasets), the smoking status for samples in the independent data set was predicted. The sensitivity, specificity, and MCC are 0.63, 0.75, 0.37 respectively.

8_. Biomarker genes:

Genes involved in modulation of the immune response in terms of immune evasion of the lung adenocarcinoma

Compared to lung adenocarcinoma of non-smokers, lung adenocarcinoma in smokers is characterized by the up-regulation of genes that are involved in cell-to-cell signaling and the modulation of the immune response: STAT1 , IFNAR2, PSME2, PSMB9, CXCL9, CXCL10, CD274, IL15, IL15RA, IL6, CCL5, CCL4, IL18RAP, IL2RB (Figure 5). The overall effect of this group of genes is to promote immune evasion of the lung carcinoma. Genes involved in apoptosis and cell survival network

Compared to lung adenocarcinoma of non-smokers, lung adenocarcinoma in smokers are characterized by the up-regulation of CDK1 , FEN1 , CCNE1 , MCM4, CDC20, BIRC5, CDK4 and GADD45B. The overall effect of these genes is to promote tumor growth by up-regulating processes involved in cell proliferation: DNA replication, cell cycle progression, mitosis, as well the repair of DNA damage (Figure 6).

Genes involved in cellular growth and proliferation network

Finally, compared to lung adenocarcinoma of non-smokers, lung adenocarcinoma from smokers are characterized by the up-regulation of: P2RY6, CFLAR, BIRC3, NFKB2, CHEK1 , CXCL12, SPP1 , LILRB2, ADA, R1PK2 and the down-regulation of SORT1. (figure 2), The overall effect of the activity of this network is to promote tumor cell survival and protect tumor cells from the activation of apoptosis and other cell death pathways.

Genes reportedly having a tumor suppressor effect when over-expressed in lung and other cancers

It was surprising to find, that some of the genes resulting from the statistical analysis were reported in the literature as having a role in tumor suppression when over- expressed in lung or other cancer tissues. For example:

PSMB9: Different studies have demonstrated a deficiency / lack of expression of HC class I molecules in the surface of many types of tumors, (Singal et al., 1996, Delp et al., 2000). PS E9 has been postulated as a tumor suppressor in cancer of the uterus (Havashi et al., 201 1)

PSME2: Similar to PSMB9, lower levels are associated with increased aggressiveness and resistance to therapy in melanoma (Stone et al., 2009) and gastric adenocarcinomas (Zheng et al., 2012).

CCL4: Increased levels of CCL4 (MIP1 b) are reported to decrease the number of metastasis in murine cell lymphoma (Menten et al., 2002) and a mouse model of lung cancer (Ishihara et al, 1998).

CCL5: Increased levels of CCL5 (RANTES) are associated with better prognosis and increased survival in lung adenocarcinoma (Moran et al., 2002, Qhri et al., 2010) IFNAR2:. Increased levels of IFNAR2 are associated with tumor suppression (Mendoza-Villanueva et al., 2008, Swann et al., 2007) yet IFNAR2 overexpression was observed in various histological types of lung cancer, and appears to be associated with lung cancers that behave aggressively (Tanaka et al., 2012). This is an example of a gene that apparently plays different roles in different settings.

CXCL9: Increased levels of CXCL9 (MIG) are reported to inhibit cell growth in mouse and human lung tumors (Addison et al., 2000, Winter et ah, 2007).

CXCL 0: Treatment with CXCL10 (INP-10) is reported to inhibit metastasis and to increase cell death in a mouse model of lung tumor (Arenberg et al., 2001) and in human non-small cell lung cancer (Ohri et al., 2010).

IL 8RAP: No conclusive data are provided in the literature on the role of for IL18 receptor.

IL2RB: Decreased levels of 1L2RB are reported to increase the number of metastasis in a mouse model of malignant pleural effusion using human adenocarcinoma cells (Arenberg et al., 2001 ).

IL15: Tumor nodule formation was retarded and tumor growth was inhibited after treatment with IL15 (Tang et al., 2008).

STAT1 : Activation of STAT1 pathway is reported to inhibit lung cancer cell proliferation, anchorage-independent growth, tumorigenesis, cell motility, and invasion, and to slow cell cycle progression (Tsai et al., 2006). Statl is further reported to inhibit lung tumor formation by activated K-Ras (Wang et al., 2008).

Tablej i List of biomarker genes specifically up regulated in lung adenocarcinomas of smokers when compared to non-smokers

Gene Log fold symbol Gene name probeset id change

interferon (alpha, beta and

1FNAR2 omega) receptor 2 204785_x_at 0.40900

small nuclear ribonucleoprotein

SNRPG polypeptide G 205644_s_at 0.44000

proteasome (prosome,

PSMB7 macropain) subunit, beta type, 7 200786_at 0.46300

polymerase (RNA) III (DNA

POLR3F directed) polypeptide F, 39 kDa 2052 8_at 0.48300

MARS methionyl-tRNA synthetase 21367 _s_at 0.49800

TGS1 trimethylguanosine synthase 1 238346_s_at 0.49800

general transcription factor HE,

GTF2E1 polypeptide 1 , alpha 56kDa 205930_at 0.50300 proteasome (prosome,

macropain) 26S subunit, non-

PSMD12 ATPase, 12 202352_s_at 0.50900 polymerase (RNA) II (DNA

POLR2H directed) polypeptide H 209302_at 0.53800 polymerase (RNA) II (DNA

POLR2E directed) polypeptide E, 25kDa 217854_s_at 0.55100

TAF5 RNA polymerase II, TATA

box binding protein (TBP)-

TAF5 associated factor, 100kDa 210053_at 0.56100

CDK4 cyclin-dependent kinase 4 202246_s_at 0.56900 small nuclear ribonucleoprotein

SNRPD1 D1 polypeptide 16kDa 202690_s_at 0.58700

TARS threonyl-tRNA synthetase 201263_at 0.59900 general transcription factor ME,

GTF2E2 polypeptide 2, beta 34kDa 202680_at 0.60200 eukaryotic translation initiation

EIF4A3 factor 4A3 201303_at 0.63300 phosphoribosylglycinamide

formyltransferase,

phosphoribosylglycinamide

synthetase,

phosphoribosylaminoimidazole

GART synthetase 2 2378_at 0.65100 proteasome (prosome,

macropain) activator subunit 2

PSME2 (PA28 beta) 201762_s_at 0.66300 poliovirus receptor-related 2

PVRL2 (herpesvirus entry mediator B) 203149_at 0.67700 small nuclear ribonucleoprotein

SNRPF polypeptide F 203832_at 0.68900

MEDI O mediator complex subunit 10 223247_at 0.69600

NBN Nibrin 202907_s_at 0.70600 transcription elongation factor B

(Sill), polypeptide 1 (15kDa,

TCEB1 elongin C) 202824_s_at 0.70600

TUBB3 tubulin, beta 3 class III 202154_x_at 0.70900

PPP3CB protein phosphatase 3, catalytic 202432_at 0.71300 subunit, beta isozyme

CASP8 and FADD-like

CFLAR apoptosis regulator 210563_x_at 0.71400 minichromosorne maintenance

MCM6 complex component 6 201930_at 0.72300 v-yes-1 Yamaguchi sarcoma

LYN viral related oncogene homolog 202625_at 0.72900 integrin, alpha 5 (fibronectin

ITGA5 receptor, alpha polypeptide) 201389_at 0.79100 cell division cycle 34 homolog

CDC34 (S. cerevisiae) 212540_at 0.79900

FYN oncogene related to SRC,

FYN FGR, YES 210105_s_at 0.88800 small nuclear ribonucleoprotein

SNRPB polypeptides B and B1 213175_s_at 0.90500 chemokine (C-X-C motif) ligand

CXCL12 12 203666_„at 0.91200

E2F transcription factor 5, p130-

E2F5 binding 22 586_s_at 0.92600 replication factor C (activator 1)

RFC3 3, 38kDa 2Q4127_at 0.93300 nuclear factor of activated T- cells, cytoplasmic, calcineurin-

NFATC1 dependent 1 210805_s_at 0.93800 lymphocyte cytosolic protein 2

(SH2 domain containing

LCP2 leukocyte protein of 76kDa) 205269_at 0.94100 receptor-interacting serine-

RIPK2 threonine kinase 2 209545_s_at 0.95600

RPL22L1 ribosomal protein L22-like 1 225541_at 0.99800 pyrimidinergic receptor P2Y, G-

P2RY6 protein coupled, 6 208373_s_at 1.00600

ADA adenosine deaminase 204639_at 1.03800 phosphoribosylaminoimidazole

carboxylase,

phosphoribosylaminoimidazole

PAICS succinocarboxamide synthetase 201013_s_at 1 .04000 signal transducer and activator

STAT1 of transcription 1 , 91 kDa 200887_ s at 1.05600 interleukin 18 receptor

1L18RAP accessory protein 207072_ at 1.05800

PTTG1 pituitary tumor-transforming 1 203554_ x at 1.06400

ODC1 ornithine decarboxylase 1 200790_ .at 1.09700

IL15RA interleukin 15 receptor, alpha 207375 s at 1.09800 nuclear factor of kappa light

polypeptide gene enhancer in B- FKB2 ce!ls 2 (p49/p100) 207535_s_at 1 .13000

WARS tryptophanyl-tRNA synthetase 200629_ _.at 1 .13800

OSMR oncostatin M receptor 226621_ _.at 1 .16900 transporter 2, ATP-binding

cassette, sub-family B

TAP2 (MDR/TAP) 225973_ _.at 1.21000

CD3d molecule, delta (CD3-

CD3D TCR complex) 213539. .at 1 .23400 transporter 1 , ATP-binding

cassette, sub-family B

TAP1 (MDR/TAP) 202307_. _s_at 1.23400 solute carrier family 7 (amino

acid transporter light chain, L

SLC7A5 system), member 5 201195_s_at 1.25200 interferon induced

IFITM1 transmembrane protein 1 214022_, _s_at 1.25700

IL2RB interleukin 2 receptor, beta 205291_at 1.26600 sphingosine-1 -phosphate

S1 PR3 receptor 3 228176_at 1.29000 flap structure-specific

FEN1 endonuclease 1 204767_. _s_at 1.34100

CCNE1 cyclin E1 213523_at 1.37000 growth arrest and DNA-damage-

GADD45B inducible, beta 207574_s_at 1.37900 fms- related tyrosine kinase 1

(vascular endothelial growth

factor/vascular permeability

FLT1 factor receptor) 222033_. _s_at 1.40300

CDK1 cyclin-dependent kinase 1 203213_. _at 1 .45400 budding uninhibited by

benzimidazoles 1 homolog

BUB1 (yeast) 209642_at 1.49500

SPP1 secreted phosphoprotein 1 209875_s_at 1.51200 baculoviral IAP repeat

BIRC3 containing 3 210538_s_at 1.51400 proteasome (prosome,

macropain) subunit, beta type, 9

(large multifunctional peptidase

PSMB9 2) 204279 at 1 .53500 minichromosome maintenance

MC 4 complex component 4 222036 s at 1 .54100 vascular cell adhesion molecule

VCAM1 1 203868_s_at 1 .55200

CHEK1 checkpoint kinase 1 205394_at 1.59500 phorbol-12-myristate-13-

P AIP1 acetate-induced protein 1 204285_s_at 1.60100

TYMS thymidylate synthetase 202589_at 1.62700 tumor necrosis factor, alpha-

TNFAIP3 induced protein 3 202644_s_at 1.63900

IL15 interleukin 15 2 7371_s_at 1.65500 ubiquitin-conjugating enzyme

UBE2C E2C 202954_at 1.76500 leukocyte immunoglobulin-like

receptor, subfamily B (with T

LILRB2 and ITI domains), member 2 207697_x_at 1.79500

CCL8 chemokine (C-C motif) ligand 8 214038_at 1.83900

CCL4 chemokine (C-C motif) ligand 4 204103_at 1.84200 cell division cycle 20 homolog

CDC20 (S. cerevisiae) 202870_s_at 1.85500

ITK IL2-inducible T-cell kinase 211339_s_at 1.87200

CD274 CD274 molecule 227458_at 1.89900

CCL5 chemokine (C-C motif) ligand 5 204655 at 1.94100 baculoviral IAP repeat

BIRC5 containing 5 202095_s_at 1.96000

AIM2 absent in melanoma 2 206513_at 2.08100 chemokine (C-X-C motif) ligand

CXCL10 10 204533_at 3.08400 chemokine (C-X-C motif) ligand

CXCL9 9 203915_ai 3.18500 chemokine (C-X-C motif) ligand

CXCL1 1 11 210163_at 3.20100

IL6 interleukin 6 (interferon, beta 2) 205207_at 3.33600 granzyme B (granzyme 2,

cytotoxic T-lymphocyte-

GZMB associated serine esterase 1 ) 210164_at 3.53000

Table 2: List of biomarker genes specifically downregulated in lung adenocarcinomas of smokers when compared to non-smokers

Gene Log fold symbol Gene name probesetjd change

aldehyde dehydrogenase 6

ALDH6A1 221588_x_at -1.318 family, member A1

4-aminobutyrate

ABAT 209460_at -1 .281 aminotransferase

acyl-CoA synthetase long-chain

ACSL5 222592_s_at -1 .207 family member 5

FUCA1 fucosidase, alpha-L- 1 , tissue 202838_at -1.089

GALC Galactosylceramidase 204417_at -0.943 aldehyde dehydrogenase 3

ALDH3A2 202053_s_at -0.912 family, member A2

GUSB glucuronidase, beta 202605 at -0.778

SORT1 sortilin 1 224818 at -0.776

MANBA mannosidase, beta A, lysosomal 203778 at -0.744 hydroxysteroid (17-beta)

HSD17B4 201413_at -0.734 dehydrogenase 4

insulin-like growth factor 2

IGF2R 201393_s_at -0.7 1 receptor

NAGLU N-acetylglucosaminidase, alpha 204360 s at -0.704

ATPase, H+ transporting,

ATP6V0A1 212383_at -0.699 lysosomal V0 subunit a1

CAT Catalase 201432_at -0.677 hexosaminidase B (beta

HEXB 201944_at -0.635 polypeptide)

SCP2 sterol carrier protein 2 21 1733 x at -0.620 hexosaminidase A (alpha

HEXA 201765_s_at -0.592 polypeptide)

ATPase, H+ transporting,

ATP6V0D1 212041_at -0.573 lysosomal 38kDa, V0 subunit d1

aldehyde dehydrogenase 9

ALDH9A1 201612_at -0.557 family, member A1

ATPase, H+ transporting,

ATP6AP1 207809_s_at -0.543 lysosomal accessory protein 1

N-acylsphingosine

ASAH1 amidohydrolase (acid 213702_x_at -0.514 ceramidase) 1

CPT2 carnitine palmitoyltransferase 2 204264 at -0.497 glucosamine (N-acetyl)-6-

GNS 212334_at -0.460 sulfatase Table 3: List of biomarker genes specifically up regulated in lung adenocarcinomas from smokers when compared to non-smokers and reported in the prior art to have a tumor suppressor effect when overexpressed in lung and other cancers.

9. Identification of Signalling Pathways and Biological Networks

Following a classical approach to gene expression analysis, genes for which the False Discovery Rate (FDR) of the interaction term is below 0.05 are reviewed first. A total of 698 differentially expressed genes were found (coefficient threshold log2(1.2) and FDR threshold 0.05). In order to refine the differentially expressed gene list, a gene set enrichment analysis of the interaction term is performed, which is advantageous as it does not depend on a cutoff value for genes and is able to capture more subtle signals (Mootha et al., Subramanian et al.). Gene set enrichment analysis (GSEA) [1 1] was used for comparison of the expression of groups of genes (gene-sets) between different tissues. GSEA provides common gene-set patterns even when single gene analysis shows only a few overlapping genes between groups. The differences in gene expression between healthy and tumor tissue in smokers and non-smokers were compared. By doing this double comparison it was possible to investigate the contribution of cigarette smoke to tumor development and progression. Using a stringent FDR cutoff of 0.01 , gene-sets were extracted for subsequent analysis. The interpretation of the result was then performed using I PA (Ingenuity Systems, www.ingenuity.com) and the gene-sets obtained from the intersection between leading edge genes and differentially expressed genes to determine the most significant biological functions and molecular pathways that are specifically activated in smoke-related adenocarcinomas, A differentially expressed gene list containing gene identifiers and corresponding fold changes was first uploaded as an Excel spreadsheet into the I PA software. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. These genes were then used as the starting point for pathway analysis. Canonical pathways analysis identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the data set. The significance of the association between the dataset and the canonical pathway was measured in two ways: (1 ) ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway was displayed, (2) Fischer's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone. Only molecules from the dataset that met the cut-off criteria (1.5 fold-change and p<0.05) were considered for the analysis.

Figure 4A shows the top ten biological functions associated with lung tumors of smokers. As expected, these include functions related to cancer, but also to cellular growth and proliferation, cell death and survival, as well as different aspects of the immune response, such as immune cell trafficking, inflammatory response, and cellular movement. Moreover, the gene-sets were categorized into canonical pathways. Figure 4B shows the top ten canonical pathways activated specifically in CS-related lung adenocarcinomas. Consistent with the previous figure, the most significant pathways are associated with the DNA damage response and the regulation of cell cycle progression, proliferation and apoptosis.

There is growing evidence indicating that the molecular pathways driving tumorigenesis in smokers and non-smokers are quite different, the former generally being more aggressive, associated with poor survival and resistance to therapy. Accordingly, it was found that lung tumors in smokers show increased activation of cell proliferation and survival pathways, as well as alterations in the immune response. To gain further insight into the molecular mechanisms driving CS-related tumor growth, the gene-sets were used to generate de novo networks based on knowledge from the literature contained in the 1PA knowledge base.

Lung adenocarcinoma is a heterogeneous disease, with specific clinical and genetic features depending on the smoking-status of the patients. Several studies indicate that the currently identified genetic mutations cannot explain the full spectrum of phenotypes observed in the clinics, suggesting a broader and more complex network of interaction among the various components. In the present study, the focus was on biological processes, rather than on individual molecules, which drive lung tumorigenesis in smokers. In particular, it was found that there is a strong activation of immune regulatory pathways in CS-induced tumors. Numerous studies demonstrated antitumoral role for both the innate and the adaptive immune responses, thus supporting the concept of immunosurveillance (Schroder et al, Vesely et al.). Further studies, however, revealed a more complex scenario where the immune system exerts a selective pressure over the nascent tumor cells, which shapes or edits tumor outgrowth, a more global concept known as immunoediting (Schroder et al, Vesely et al.). When the immune system fails to detect and / or control transformed cells, the tumor is able to survive and continue growing. While in some cases this is the result of compromised immune response (i.e. immunosuppression after an organ transplant), more and more evidence supports a more active role of the tumor in the immune evasion process, either by inhibiting tumor recognition and cyto lysis by immune effector cells, by inducing immunotolerance, or by creating an immunosuppressive tumor microenvironment (Zaidi et al., Schroder et al., Manjili et al.). Our results suggest that multiple immune response pathways are upregulated in smoke-related lung tumors. Individually, most of these cell signaling pathways would prevent tumor growth, however, unexpectedly their combined actions, together with the effects of CS exposure results in an intricate immune response that allows immune evasion and favors tumor cell proliferation. Tumor growth is further enhanced by overactivation of cell survival pathways and inhibition of apoptosts. The findings are of potential clinical interest. On one hand, they can provide the basis for the identification of novel therapeutic targets in the treatment of lung adenocarcinoma. On the other hand, they could help select the most appropriate treatment for the patient, thus maximizing the chances for success to therapy.

Cell-to-cell signaling network The first network contains a number of molecules that are essential in cell-to-cell signaling and the modulation of the immune response (Figure 5). The central molecules are: IL15 (interleukin 15), IL6 (interleukin 6) and STAT1 (signal transducer activator of transcription 1), IL15 is a pro-inflammatory cytokine expressed in many tissues including lung epithelium. Some of its downstream signaling elements include JAK/STAT, APK and PI3K/AKT pathways, which promote the proliferation and activation of natural killer (NK) cells as well as T and B lymphocytes (Carson et al (1994 and 1997), Schluns et al.) thus initiating a strong immunological response. This response is further enhanced by STAT1 , an essential mediator of interferon gamma (IFNG), and its downstream targets IL6, CCL4 (C-C motif ligand 4) and CCL5 (C-C motif ligand 5). These cytokines and chemokines further promote the recruitment of lymphocytes, monocytes and NK cells. IL15 and STAT1 also play an important role in the development antitumoraf immune responses. In fact, due to its ability to stimulate strong T-cetl mediated cytotoxic responses IL15 is currently being evaluated as an immunotherapeutic agent (Le Maux Chansac et al., Takeuchi et al., Teague et al.). Furthermore, STAT1 can induce growth arrest and apoptosis in cancer cells through the activation of p27 (Wang et al ), p2 ^WAF and caspases (Yu et al.).

Because of their tumor suppressive effects, immune mediators are often inhibited or attenuated in lung and other malignancies. Indeed, this is the case in tumors from non-smokers, where down regulation was observed in the expression of IL15, STAT1, CCL4 and CCL5 genes in tumors compared to healthy tissue. Surprisingly, and despite generally being more aggressive, lung tumors in smokers showed an upregulation of these four genes when compared to non-smokers.

In order to better understand the role of IL15 and STAT1 in the development and progression of lung adenocarcinomas in smokers, the network was evaluated as a whole, taking into account multiple interactions among the different nodes. The rationale for this approach is that immune regulators show a high level pleiotropy and crosstalk among the different signaling pathways which may lead to different outcomes depending on the cellular context. IL15, for example, can be found soluble, but the majority of the protein is bound to the cellular membrane, either directly or through an interaction with 1L15RA (IL15 receptor subunit alpha) (Jakobisiak et al ). Pathway activation requires the binding of IL15 to the IL15 receptor complex. Under normal circumstances, this complex is formed by the alpha subunit in the host cell and IL2RB and IL2RG (IL2 receptor subunits beta and gamma, respectively) in the target cell. In renal and other carcinomas, however, IL15 can bind to an aberrant form of the receptor complex formed by alpha and beta chains alone. This aberrant conformation then promotes epithelial to mesenchymal transition (G iron-Michel et al.) and cell proliferation and transformation (Motegi et al). Formation of this aberrant receptor is favored by increased levels of IL15 and the alpha and beta subunits of its receptor (Giron-Michel et al., Khawam et al.). Although increased gene expression does not necessarily translate into increased protein levels, the observed upregulation of IL15, IL15RA and IL2RB genes in lung tumors from smokers strongly suggests the presence of an aberrant IL15 signaling in lung tumors from smokers.

IL15 also increases the expression of IL18RAP (interleukin 18 receptor accessory protein), an enhancer of IL18 receptor signaling that promotes NFKB activation (Born et al ). IL15/IL18RAP activation induces the release of IFNG (Sareneva et al.), which, in turn, regulates the expression of many genes either directly or through activation of JAK/STAT pathways. Interferons are a class of cytokines that modulate innate and adaptive immune responses against viruses, bacteria and tumor cells through an effect on NK cells and cytotoxic T lymphocytes. In particular, INFG has important immunomodulatory functions, including T-cell differentiation, activation and homeostasis, NK cell activation, lysosomal activation in macrophages, and the promotion of antigen presentation by upregulating the expression of components of the class I major histocompatibility complex (MHC) and of the immunoproteasome (Schroder et al.). A priori, all these effects support a role for IFNG in tumor cell identification and elimination. Indeed, a number of studies suggest that IFNG plays a key role in tumor surveillance by increasing tumor immunogenicity (Dunn et al., Zaidi et al.), inhibiting tumor cell proliferation and promoting apoptosis (Zaidi et al., Ikeda et al.). In fact, the therapeutic use of IFNG has already been tested in the clinic with melanoma patients, although it showed low efficacy (Schiller et al.) and surprisingly, even faster disease progression compared to non-treated patients (Meyskens et al.). In the recent years, a number of studies have demonstrated a dual role for IFNG and the immune response on tumor development and progression. While IFNG has the potential to drive an antitumor immune response, increased / sustained IFNG activation is able to inhibit apoptosis and promote tumor cell proliferation and metastasis (Zaidi et al.).

In the present study, it was not possible to detect changes in IFNG gene expression per se. However, by using INTERFEROME database (Samarajiwa et al.), it was possible to identify a high number of IFNG target genes upregulated in lung tumors from smokers compared to non-smokers (Figure 5), which strongly supports the idea of a sustained IFNG activation. Increased expression of CCL4, CCL5 and the C-X-C motif chemokines CXCL9 and CXCL10 in tumors from smokers were observed. These molecules are potent regulators of monocytes / macrophages, T-lymphocytes, dendritic cells and NK cell activation. IFNG-mediated overexpression of these proteins is associated with alveolar destruction and emphysema (Ma et al ). Elevated levels of CXCL9 and CXCL10 can also be detected at metastatic sites in lung and colorectal carcinomas, where they promote MMP9 (matrix metalloprotease 9) mediated disruption of the endothelial barrier and cellular migration. Interestingly, CS dramatically up regulates IFNG (Ma et al.) and thus, the levels of CXCL9 and CXCL10. Increased levels of these chemokines will likely result in more tissue destruction and putative ly a higher risk for invasion and metastasis.

Previous reports have shown that IFNG allows tumors to escape to immune surveilance.by upregulating the expression of MHC-I and MHC-II molecules, leading to decreased NK-mediated celly lysis and T-cell mediated apoptosis (Zaidi et al., Maio et al., Hemon et al, Beatty et al.). Similarly, in lung tumors from smokers an upregulation was observed of several genes involved in antigen presentation by MHC-I and that are downstream targets of IFNG (Figure 5). It was hypothesized that these tumors have active mechanisms that allow them to escape elimination by the immune system. IFNG upregulates the expression of IFNAR2 (interferon alpha/beta receptor beta chain) leading to activation of JAK STAT1 pathways and IL6 overexpression. IFNAR2 also promotes the activation of the immunoproteasome, thus increasing antigen presentation in tumor cells. The immunoproteasome is further activated by the upregulation of two other relevant genes, PSME2 (proteasome activator complex subunit 2) and PSMB9 (proteasome subunit beta type-9). Thus, overactivation of the immunoproteasome and excessive antigen presentation can, in fact, induce a downregulation of the immune response. NK cells respond to infected or transformed cells either by killing the abnormal cells or by releasing immunomodulatory chemokines and cytokines such as IFNG. In order to prevent damage to normal tissues, NK cells are normally restrained by different types of inhibitory receptors that recognize MHC class I molecules in the target-cell. When MHC-I expression is low, NK cells are liberated from the inhibitory receptors and can kill target cells more efficiently (French et al.). Conversely, high expression of PSME2 and PSMB9 leads to increased MHC-I expression and increases in the chances of interaction with inhibitory receptors in the NK cell, thus preventing tumor cell elimination. Therefore, upregulation of MHC-I molecules would be a very ingenious mechanism for tumors to evade immune surveillance. This type of response has been already reported in hematopoietic malignancies and is mediated by STAT (Kovacic et al.). Furthermore, CD274 was found (also known as programmed cell death ligand 1 , PD-L1 ) also upregulated in our network. In response to IFNG, CD274 sends an inhibitory signal that inhibits the proliferation of activated T-lymphocytes, monocytes NK and dendritic cells (Riley et al.). The main role of CD274 is to induce fetomaternal tolerance during pregnancy. CD274 overexpression correlates with poor prognosis in non-small cell lung cancer (NSCLC), where induces immune escape by preventing the maturation of dendritic cells (Mu et al.). Finally, high levels of IFNAR2, PMSE2 and PMSB9 lead to sustained activation of p38/MAPK and NFKB pathways. As a central mediator of inflammation, NFKB can promote cellular death or survival. NFKB regulates cellular fate in connivance with the tumor suppressor gene TP53. TP53 mutations can be found in lung adenocarcinomas from both smokers and non- smokers, but the percentage is significantly higher in smokers, and can be directly linked to CS exposure (Couraud et al.). While in a p53 proficient cell, sustained NFKB would cause growth arrest and apoptosis, in lung tumors from smokers the opposite effect (increased proliferation and cell survival) would be expected.

Cell proliferation and survival network

A number of upregulated of genes was found that control cell proliferation and survival in CS-related lung tumors. Tumors are by definition, an abnormal and uncontrolled growth of a specific tissue. Therefore it is not surprising to observe activation of molecular pathways that promote cell proliferation and /or prevent cellular death. The interesting observation, however, is that these genes are upregulated not only in tumor compared to healthy tissue, but more importantly in lung tumors from smokers compared to non-smokers. In addition, most of the upregulated genes have been previously associated with poor outcome in multiple types of cancer, including those of the lung, supporting the more aggressive phenotype often observed in CS-related lung tumors.

I PA was used to build a cell proliferation and survival network and to investigate the interactions among its components. The network is divided into two subnetworks. At the core of the first subnetwork (Figure 6A) lies CDK1, (cyclin dependent kinase 1 ) the only mitotic kinase that is both necessary and sufficient to drive cell division (Santamaria et al., Linares et al.). CDK1 interacts directly with other key regulators of cell cycle progression, such as CDC20 (cell divison cycle 20 homolog), BIR5 (baculoviral inhibitor of apoptosis repeat-containing 5 or survivin) and FEN1 (Flap structure-specific endonuclease 1 ). CDC20, a member of the anaphase promoting complex, is responsible for the inhibition / degradation of many cell cycle regulators like p21^ARF (i.e. inhibits CDK1 complex formation, cyclin E, Cyclin D and PCNA) (Kato et al., Qiao). BIRC5 is an inhibitor of apoptosis by inhibiting BAX and FAS-mediated pathways (Tamm et al.) and a transcriptional inhibitor of the P21WAF1 gene (Tang et al.). FEN1 is involved in the processing of the Okazaki fragments in the DNA lagging strand synthesis (Henneke et al.), telomere stability (Saharia et al.) and DNA- repair pathways (Klungland et al.), where is considered a limiting factor. Another important component of the network is CCNE1 (Cyclin E1) which serves as a regulator of cyclin-dependent kinases and the RB family of proteins, thus promoting G1/S transition and cell cycle progression (Harbour et al., Shanahan et al.). CCNE1 overexpression is particularly common in smoke-related lung tumors where it shortens cell cycle and promotes genomic instability (Ohtsubo et al., Spruck et al.). The presence of MCM4 (minichromosome maintenance protein 4) is also not surprising in this network, as it is considered a marker of poor prognosis in NSCLC and other tumors (Freeman et al., Kikuchi et al.). As a key component of the p re- replication complex, MCM4 is essential for genome replication (You et al ). Uncontrolled proliferation is further promoted by overexpression of CDK4 (cyclin dependent kinase 4). CDK4 promotes G1/S transition, partially by phosphorylating RB1 , which may explain at least in part, the impairment of the retinoblastoma (RB) pathway known to occur in most lung tumors (Wikman et al.). Interestingly, an important inhibitor of this pathway, CDKN2A, is most often mutated in lung adenocarcinomas (Ding et al., Imietinski et al.), suggesting the existence of constitutive activated cyclin-dependent growth signals.

The effect of this network is to boost tumor cell proliferation. Exaggerated cell growth increases the risk for accumulating DNA mutations leading to genomic instability and compromised cellular function. In fact, many tumors show both high rates of cell proliferation and apoptosis, and the growth rate of the tumor depends on the ratio between these two processes. This is not the case for CS-related lung tumors, where higher proliferative potential is accompanied by activation of different pathways promoting cell survival, as depicted in the second subnetwork (Figure 6B).

On one hand, the upregulation of genes with a key role in DNA repair was observed, such as FEN1 and CHEK1. Besides its role in DNA replication, FEN1 participates in the repair of different types of DNA damage, including replication-induced single and double-strand DNA breaks and it is considering a limiting factor in DNA repair (Nikoiova et al.). CHEK1 is a critical component of DNA replication, intra-S phase, G2/M transition, and mitotic spindle-assembly checkpoints (Bartek et al.). In response to DNA damage, it becomes activated and blocks cell cycle progression until the damage is repaired (Bartek et al., Peng et al.). If the DNA lesion cannot be finally repaired, normal cells activate senescence/cell death programs to prevent further damage. However, when tumor suppressor genes such as TP53 and RB family members are mutated, as it is often the case in lung and other types of cancer, the activation of these programs is compromised and the cells are more likely to escape cell death.

On the other hand, it was found that tumors of smokers activate pathways that inhibit apoptosis and promote cell survival (Figure 6B). SPP1 (secreted phosphoprotein 1 or osteopontin) is an important bone remodeling factor often overexpressed in tumors of the lung, breast and colon that is associated with poor prognosis (Shevde et al.). SPP1 is also upregulated in response to CS (Bishop et al.) and has been suggested to promote preneoplastic cell growth in vivo and in vitro (Pazoili et al.). SPP1 is able to inhibit BAX activation and prevent BCL2 down regulation, thus inhibiting caspase-9 and caspase-3 dependent cell apoptosis (Gu et al.). Based on the present network, cell survival would be further reinforced by the overexpression of the caspase inhibitors CFLAR (CASP8 and FADD-like apoptosis regulator) and BIRC3 (baculoviral inhibitor of apoptosis repeat-containing 3). Furthermore, constitutive activation of NFKB, either through activating mutations or downregulation of its inhibitors (IKBs) is commonly found in lung and other tumors and often associated with increased resistance to therapy (Rayet et al.). NFKB may be upregulated in tumors from smokers. First, NFKB2 (the p100 subunit of NFKB) is upregulated in smokers and its expression is directly induced by CS. Second, RIPK2 (Receptor- interacting serine/threonine-protein kinase 2) is a potent activator of NFKB through the inhibition of ikbs complexes (Kim et al.). Finally, BIRC3 and BIRC5 have recently been identified as the E3-ubiquitin ligases responsible for of RIPK2 ubiquitinilation, a prerequisite for NFKB activation (Bertrand et at.). The combined effect of these two subnetworks is to provide the tumor with a formidable proliferative potential, while protecting cancer cells from any attempt of activation of senescence or apoptosis programs that may limit its growth.

In order to investigate whether the regulation of the immune response and cell-to-cell signaling and the cell proliferation and survival networks represent independent processes or they are being regulated in a coordinated manner, upstream regulator analysis in tPA was performed and identified several candidates. From all of them, IFNG, TNF (tumor necrosis factor) and IL1 B (interleukin 1 beta) were selected based on their z-score, p value and number of downstream targets to expand our networks and find possible interactions between them. Although IFNG, TNF, or IL1 B were not upregulated in tumors of smokers compared to non-smokers (likely due to high level of expression in both tumor tissues), they interact strongly with multiple components of our networks (Figure 7) suggesting not only a major role for all three molecules but also that the different activated pathways are acting in a coordinated manner in order to promote tumor growth.

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Claims

1. A method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value for the expression of a biomarker; and ii. determining in a sample taken from a non-smoker suffering from lung cancer and/or from a smoker suffering from lung cancer a reference value for the expression of a biomarker, and iii. comparing the test values obtained for each biomarker with the reference values obtained from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer,

wherein said biomarker corresponds to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally, in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2.

2. The method of claim 1 of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ), and, optionally, further comprising in combination with at least 1 and up to 108 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to at least 1 and up to 108 genes, which are different from the above identified genes and are depicted in table 1 , table 2, or both table 1 and table 2; and ii. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); and iii, comparing the test values obtained for each biomarker in the composition, with reference values for the biomarkers from lung cancer of a non-smoker and/or a smoker suffering from lung cancer.

3. A method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ) and 209545_s_at (RIPK2) or 204767_s_at (FEN1) and 213523_at (CCNE1) or 209545_s_at (RIPK2) and 213523_at (CCNE1 ), and, optionally, further comprising at least 1 and up to 108 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to at least 1 and up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2; and ii. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); and iii, comparing the values obtained for each biomarker in the composition, with reference values for the biomarkers from lung cancer of a non-smoker and/or a smoker.

4. A method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i, determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value of the expression of a biomarker comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), and, optionally, further comprising at least 1 and up to 108 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to at least 1 and up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2; and

it. determining a reference value in a sample taken from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer comprising using a composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide corresponding to the genes identified in section i); and iii. comparing the values obtained for each biomarker in the composition, with reference values for the biomarkers from lung cancer of a non-smoker and/or a smoker.

5. The method of any one of claims 1 to 4, wherein differential expression is determined by way of comparison to a reference value obtained from an individual, which is a non-smoker and/or a smoker, who suffers from lung cancer or from a population of non-smokers and/or smokers who have lung cancer.

6. The method of any of the preceding claims, wherein a difference or a similarity in the test values and the reference values is used to forecast or classify the subject into a poor survival group or a good survival group.

7. The method of claim 6, wherein a test value which is (a) similar to the reference value obtained for a smoker with lung cancer signifies a poor prognosis, whereas a test value which is (b) similar to the reference value obtained for a non-smoker with lung cancer signifies an improved prognosis.

8. A method for predicting or monitoring the outcome of a lung cancer treatment in an individual suffering from lung cancer comprising

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1) optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2; ii. determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (0; and

wherein said control individual is a non-smoker suffering from lung cancer, wherein differences between the test values and the reference values indicate the probability of said individual's lung cancer sharing one or more treatment outcomes of smoking-related lung cancer.

9. A method for predicting or monitoring the outcome of a lung cancer treatment in an individual suffering from lung cancer comprising

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1 ), or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2; ii, determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (i); and iii. comparing the values obtained for each said biomarker genes with the reference value; wherein said control individual is a non-smoker suffering from lung cancer, wherein differences between the test values and the reference values indicate the probability of said individual's lung cancer sharing one or more treatment outcomes of smoking-related lung cancer.

10. A method for predicting or monitoring the outcome of a lung cancer treatment in an individual suffering from lung cancer comprising

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of the genes identified by GeneBank Accession numbers 204767_s_at (FE 1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2; ii. determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (i); and iii. comparing the values obtained for each said biomarker genes with the reference value;

11. A method for monitoring the progress of a lung cancer treatment in an individual, said method comprising determining at suitable time intervals before, during, or after lung cancer therapy, particularly at different time points during the treatment, in a sample taken from said individual, differential expression of biomarker genes comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) optionally in combination with at least 1 and up to 108 different biomarkers selected from the biomarkers depicted in Table 1 , Table 2, or both Table 1 and Table 2.

12. A method for monitoring the progress of a lung cancer treatment in an individual, said method comprising determining at suitable time intervals before, during, or after lung cancer therapy, particularly at different time points during the treatment, in a sample taken from said individual, differential expression of biomarker genes comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a gene identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1), or 209545_s_at (RIPK2) and 213523_at (CCNE1 ), optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2.

13. A method for monitoring the progress of a lung cancer treatment in an individual, said method comprising determining at suitable time intervals before, during, or after lung cancer therapy, particularly at different time points during the treatment, in a sample taken from said individual, differential expression of biomarker genes comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a gene identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) optionally in combination with at least 1 and up to 108 different biomarkers selected from the biomarkers depicted in Table 1 , Table 2, or both Table 1 and Table 2.

14. The method of claim 13, comprising

i. determining in a plurality of biological samples a value for the expression of each of the at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) optionally in combination with 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, wherein said plurality of biological samples are obtained at a plurality of time points from an individual having lung cancer and receiving a lung cancer therapy;

ii. comparing the values obtained for each said biomarker genes with values of each said biomarker gene obtained at another time point and optionally with reference values for each said biomarker gene of a non- smoker suffering from lung cancer; wherein differences between the test values obtained at the plurality of time points, or changes in the differences between the test values and the reference values provide a prediction of the outcome of the lung cancer therapy,

15. The method of claim 14, comprising

i. determining in a plurality of biological samples a value for the expression of each of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1), or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, wherein said plurality of biological samples are obtained at a plurality of time points from an individual having lung cancer and receiving a lung cancer therapy;

ii. comparing the values obtained for each said biomarker genes with values of each said biomarker gene obtained at another time point and optionally with reference values for each said biomarker gene of a non- smoker suffering from lung cancer; wherein differences between the test values obtained at the plurality of time points, or changes in the differences between the test values and the reference values provide a prediction of the outcome of the lung cancer therapy.

16. The method of claim 15, comprising

i. determining in a plurality of biological samples a value for the expression of each of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) optionally in combination with 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, wherein said plurality of biological samples are obtained at a plurality of time points from an individual having lung cancer and receiving a lung cancer therapy;

17. The method according to any one of the preceding claims, wherein the sample is selected from blood, serum, plasma, sputum, saliva, tissue particularly lung tissue, obtained through biopsy, bronchia brushings, exhaled breath, or urine.

18. The method according to any one of the preceding claims, wherein determination of biomarker values is accomplished by performing an in-vitro assay, particularly an in-vitro assay selected from the group consisting of an antibody-based assay such as an immunoassay, a histological or cytological assay, an expression level assay such as an RNA expression level assay and an aptamer-based assay.

19. The method according to any one of the preceding claims, wherein a. a polynucleotide or a variant thereof, which is complementary to a target gene as depicted in table 1 , table 2, or both table 1 and table 2, is used as a molecular probe in a hybridization reaction or as a molecular primer in a nucleic acid extension reaction, for the determination of the target and reference value. b. one or more detectably labeled antibodies are used in the determination of the target and reference value, which antibodies are capable of identifying biomarker gene products encoded by one or more biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, or by conserved variants or peptide fragments thereof.

20. The method according to any one of the preceding claims, wherein at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) and optionally in combination with at least 1 biomarker or a composition, is used comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204 03_at (CCL4), 207072_at (IL18RAP), and 205291_at (IL2RB); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 2 0563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), and 2248 8_at (SORT1).

21. The method according to any one of the preceding claims, wherein 204767_s_at (FEN1 ) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1 ), or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 biomarker or a composition is used comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1 ), 222036_s_at (MCM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or (c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1 ).

22. The method according to any one of the preceding claims, wherein the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) and optionally in combination with at least 1 biomarker or a composition, is used comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to a different gene

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 20 762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291_at (IL2RB); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 2 0538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1 ), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1 ).

23. A composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN 1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1) and optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2.

24. A composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to biomarker genes 204767_s_at (FEN1) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1 ), or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2.

25. A composition comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1 ) and optionally in combination with at least 1 and up to 108 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2.

26. The composition of any one off the preceding claims, wherein said biomarker polynucleotide is

a. a polynucleotide or a variant thereof, which is complementary to a target gene as depicted in table 1 , table 2, or both table 1 and table 2 and can be used as a hybridization probe or a primer; or

b. one or more detectably labeled antibodies, which antibodies are capable of identifying biomarker gene products encoded by one or more biomarker genes depicted in table 1 , table 2, or both table 1 and table 2, or by conserved variants or peptide fragments thereof.

27. The composition of any one of the preceding claims, wherein said genes are at least 2 of the genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 biomarker gene

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1), 204785_x_at (1FNAR2); 201762_s_at (PS E2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (1L18RAP), and 205291_at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1),, 222036_s_at (MCM4), 202870__s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL 2), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1 ).

28. The composition of any one of the preceding claims, wherein said genes are the genes identified by GeneBank Accession numbers 204767_s_at (FEN1) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1 ), or 209545_s_at (RIPK2) and 213523_at (CCNE1 ), and optionally in combination with at least 1 biomarker gene

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 2039 5_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (IL6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 205291 _at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1),, 222036_s_at (MCM4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1 ), 203666_at (CXCL12), 209875_s_at (SPP1 ), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1).

29. The composition of any one of the preceding claims, wherein said genes are the genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 gene

(a) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 200887_s_at (STAT1 ), 204785_x_at (IFNAR2); 201762_s_at (PSME2), 204279_at (PSMB9), 203915_at (CXCL9), 204533_at (CXCL10), 227458_at (CD274), 207375_s_at (IL15), 207375_s_at (IL15RA), 205207_at (1L6), 204655_at (CCL5), 204103_at (CCL4), 207072_at (IL18RAP), and 20529 _at (IL2RB); or

(b) selected from the group of biomarker genes depicted in table 1 identified by GeneBank Accession number 203213_at (CDK1),, 222036_s_at (MC 4), 202870_s_at (CDC20); 202095_s_at (BIRC5), 202246_s_at (CDK4), and 207574_s_at (GADD45B); or

(c) selected from the group of biomarker genes depicted in table 1 and table 2 identified by GeneBank Accession number 208373_s_at (P2RY6), 210563_x_at (CFLAR), 210538_s_at (BIRC3), 207535_s_at (NFKB2), 203868_s_at (CHEK1), 203666_at (CXCL12), 209875_s_at (SPP1), 207697_x_at (LILRB2), 204639_at (ADA), and 224818_at (SORT1).

30. A kit for determining in an individual lung cancer or for monitoring the progress of a lung cancer treatment in an individual, comprising reagent for detecting differential expression of at least 2 of the biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2.

31. A kit for determining in an individual lung cancer or for monitoring the progress of a lung cancer treatment in an individual, comprising reagent for detecting differential expression of a a biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1 ) and 209545_s_at (RIPK2), or 204767_s_at (FEN1) and 213523_at (CCNE1 ), or 209545_s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 108 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2.

32. A kit for determining in an individual lung cancer or for monitoring the progress of a lung cancer treatment in an individual, comprising reagent for detecting differential expression of a biomarker genes identified by GeneBank Accession numbers 204767_s_at (FEN1), 209545__s_at (RIPK2) and 213523_at (CCNE1), optionally in combination with at least 1 and up to 08 genes, which are different from the above identified genes and are depicted depicted in table 1 , table 2, or both table 1 and table 2.

33. A method of providing a prognosis on the development of lung cancer in an individual, said method comprising the steps of i. determining in a sample taken from an individual, whose prognosis on lung cancer is to be provided, a test value for the expression of a biomarker; and ii. determining in a sample taken from a non-smoker suffering from lung cancer and/or from a smoker suffering from lung cancer a reference value for the expression of a biomarker, and iii. comparing the test values obtained for each biomarker with the reference values obtained from a non-smoker suffering from lung cancer and/or a smoker suffering from lung cancer, wherein said biomarker corresponds to at least 2 and up to 1 1 1 genes depicted in table 1 , table 2, or both table 1 and table 2.

34. A method for predicting or monitoring the outcome of a lung cancer treatment in an individual suffering from lung cancer comprising

i. determining in a biological sample of an individual who suffers from lung cancer a test value for the expression of each of at least 2 and up to 1 1 1 different biomarker genes depicted in table 1 , table 2, or both table 1 and table 2; ii. determining in a biological sample of a control individual a reference value for the expression of each of the same biomarker genes used in step (i); and

wherein said control individual is a non-smoker suffering from lung cancer, wherein differences between the test values and the reference values indicate the probability of said individual's lung cancer sharing one or more treatment outcomes of smoking-related lung cancer. , A method for monitoring the progress of a lung cancer treatment in an individual, said method comprising determining at suitable time intervals before, during, or after lung cancer therapy, particularly at different time points during the treatment, in a sample taken from said individual, differential expression of biomarker genes comprising using a composition, comprising at least 2 different isolated nucleic acid molecules each of which comprising a biomarker polynucleotide that corresponds to at least 2 and up to 11 1 different biomarkers selected from the biomarkers depicted in Table 1 , Table 2, or both Table 1 and Table 2.