Patent Application
20190376148
The present invention is directed to diagnostic and prognostic methods by using analysis of gene group expression patterns in a subject. More specifically, the invention is directed to diagnostic and prognostic methods for detecting lung diseases, particularly lung cancer in subjects, preferably humans that have been exposed to air pollutants.
Lung disorders represent a serious health problem in the modern society. For example, lung cancer claims more than 150,000 lives every year in the United States, exceeding the combined mortality from breast, prostate and colorectal cancers. Cigarette smoking is the most predominant cause of lung cancer. Presently, 25% of the U.S. population smokes, but only 10% to 15% of heavy smokers develop lung cancer. There are also other disorders associated with smoking such as emphysema. There are also health questions arising from people exposed to smokers, for example, second hand smoke. Former smokers remain at risk for developing such disorders including cancer and now constitute a large reservoir of new lung cancer cases. In addition to cigarette smoke, exposure to other air pollutants such as asbestos, and smog, pose a serious lung disease risk to individuals who have been exposed to such pollutants.
Approximately 85% of all subjects with lung cancer die within three years of diagnosis. Unfortunately survival rates have not changed substantially of the past several decades. This is largely because there are no effective methods for identifying smokers who are at highest risk for developing lung cancer and no effective tools for early diagnosis.
The methods that are currently employed to diagnose lung cancer include chest X-ray analysis, bronchoscopy or sputum cytological analysis, computer tomographic analysis of the chest, and positron electron tomographic (PET) analysis. However, none of these methods provide a combination of both sensitivity and specificity needed for an optimal diagnostic test.
Classification of human lung cancer by gene expression profiling has been described in several recent publications (M. Garber, “Diversity of gene expression in adenocarcinoma of the lung,” PNAS, 98(24): 13784-13789 (2001); A. Bhattacharjee, “Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses,” PNAS, 98(24):13790-13795 (2001)), but no specific gene set is used as a classifier to diagnose lung cancer in bronchial epithelial tissue samples.
Moreover, while it appears that a subset of smokers are more susceptible to, for example, the carcinogenic effects of cigarette smoke and are more likely to develop lung cancer, the particular risk factors, and particularly genetic risk factors, for individuals have gone largely unidentified. Same applies to lung cancer associated with, for example, asbestos exposure.
Therefore, there exists a great need to develop sensitive diagnostic methods that can be used for early diagnosis and prognosis of lung diseases, particularly in individuals who are at risk of developing lung diseases, particularly individuals who are exposed to air pollutants such as cigarette/cigar smoke, asbestos and other toxic air pollutants.
The present invention provides compositions and methods for diagnosis and prognosis of lung diseases which provides a diagnostic test that is both very sensitive and specific.
We have found a group of gene transcripts that we can use individually and in groups or subsets for enhanced diagnosis for lung diseases, such as lung cancer, using gene expression analysis. We provide detailed guidance on the increase and/or decrease of expression of these genes for diagnosis and prognosis of lung diseases, such as lung cancer.
One example of the gene transcript groups useful in the diagnostic/prognostic tests of the invention are set forth in Table 6. We have found that taking groups of at least 20 of the Table 6 genes provides a much greater diagnostic capability than chance alone.
Preferably one would use more than 20 of these gene transcript, for example about 20-100 and any combination between, for example, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. Our preferred groups are the groups of 96 (Table 1), 84 (Table 2), 50 (Table 3), 36 (Table 4), 80 (Table 5), 535 (Table 6) and 20 (Table 7). In some instances, we have found that one can enhance the accuracy of the diagnosis by adding certain additional genes to any of these specific groups. When one uses these groups, the genes in the group are compared to a control or a control group. The control groups can be non-smokers, smokers, or former smokers. Preferably, one compares the gene transcripts or their expression product in the biological sample of an individual against a similar group, except that the members of the control groups do not have the lung disorder, such as emphysema or lung cancer. For example, comparing can be performed in the biological sample from a smoker against a control group of smokers who do not have lung cancer. When one compares the transcripts or expression products against the control for increased expression or decreased expression, which depends upon the particular gene and is set forth in the tables—not all the genes surveyed will show an increase or decrease. However, at least 50% of the genes surveyed must provide the described pattern. Greater reliability if obtained as the percent approaches 100%. Thus, in one embodiment, one wants at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, or 99% of the genes surveyed to show the altered pattern indicative of lung disease, such as lung cancer, as set forth in the tables, infra.
In one embodiment, the invention provides a group of genes the expression of which is altered in individuals who are at risk of developing lung diseases, such as lung cancer, because of the exposure to air pollutants. The invention also provides groups of genes the expression of which is consistently altered as a group in individuals who are at risk of developing lung diseases because of the exposure to air pollutants.
The present invention provides gene groups the expression pattern or profile of which can be used in methods to diagnose lung diseases, such as lung cancer and even the type of lung cancer, in more than 60%, preferably more than 65%, still more preferably at least about 70%, still more preferably about 75%, or still more preferably about 80%-95% accuracy from a sample taken from airways of an individual screened for a lung disease, such as lung cancer.
In one embodiment, the invention provides a method of diagnosing a lung disease such as lung cancer using a combination of bronchoscopy and the analysis of gene expression pattern of the gene groups as described in the present invention.
Accordingly, the invention provides gene groups that can be used in diagnosis and prognosis of lung diseases. Particularly, the invention provides groups of genes the expression profile of which provides a diagnostic and or prognostic test to determine lung disease in an individual exposed to air pollutants. For example, the invention provides groups of genes the expression profile of which can distinguish individuals with lung cancer from individuals without lung cancer.
In one embodiment, the invention provides an early asymptomatic screening system for lung cancer by using the analysis of the disclosed gene expression profiles. Such screening can be performed, for example, in similar age groups as colonoscopy for screening colon cancer. Because early detection in lung cancer is crucial for efficient treatment, the gene expression analysis system of the present invention provides a vastly improved method to detect tumor cells that cannot yet be discovered by any other means currently available.
The probes that can be used to measure expression of the gene groups of the invention can be nucleic acid probes capable of hybridizing to the individual gene/transcript sequences identified in the present invention, or antibodies targeting the proteins encoded by the individual gene group gene products of the invention. The probes are preferably immobilized on a surface, such as a gene or protein chip so as to allow diagnosis and prognosis of lung diseases in an individual.
In one embodiment, the invention provides a group of genes that can be used as individual predictors of lung disease. These genes were identified using probabilities with a t-test analysis and show differential expression in smokers as opposed to non-smokers. The group of genes comprise ranging from 1 to 96, and all combinations in between, for example 5, 10, 15, 20, 25, 30, for example at least 36, at least about, 40, 45, 50, 60, 70, 80, 90, or 96 gene transcripts, selected from the group consisting of genes identified by the following GenBank sequence identification numbers (the identification numbers for each gene are separated by “;” while the alternative GenBank ID numbers are separated by “///”): NM_003335; NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_001319; NM_006545.1; NM_021145.1; NM_002437.1; NM_006286; NM_001003698 /// NM_001003699 /// NM_002955; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_001696; NM_005494 /// NM_058246; NM_006534 /// NM_181659; NM_006368; NM_002268 /// NM_032771; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_006694; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_004691; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_012394; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_021800; NM_016049; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_138387; NM_024531; NM_000693; NM_018509; NM_033128; NM_020706; AI523613; and NM_014884, the expression profile of which can be used to diagnose lung disease, for example lung cancer, in lung cell sample from a smoker, when the expression pattern is compared to the expression pattern of the same group of genes in a smoker who does not have or is not at risk of developing lung cancer.
In another embodiment, the gene/transcript analysis comprises a group of about 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100, 100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190, 190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260, 260-270, 270-280, 280-290, 290-300, 300-310, 310-320, 320-330, 330-340, 340-350, 350-360, 360-370, 370-380, 380-390, 390-400, 400-410, 410-420, 420-430, 430-440, 440-450, 450-460, 460-470, 470-480, 480-490, 490-500, 500-510, 510-520, 520-530, and up to about 535 genes selected from the group consisting of genes or transcripts as shown in the Table 6.
In one embodiment, the genes are selected from the group consisting of genes or transcripts as shown in Table 5.
In another embodiment, the genes are selected from the genes or transcripts as shown in Table 7.
In one embodiment, the transcript analysis gene group comprises a group of individual genes the change of expression of which is predictive of a lung disease either alone or as a group, the gene transcripts selected from the group consisting of NM_007062.1; NM_001281.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; NM_002268 /// NM_032771; NM_007048 /// NM_194441; NM_006694; U85430.1; NM_004691; AB014576.1; BF218804; BE467941; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_021971.1; NM_014128.1; AA133341; AF198444.1.
In one embodiment, the gene group comprises a probe set capable of specifically hybridizing to at least all of the 36 gene products. Gene product can be mRNA which can be recognized by an oligonucleotide or modified oligonucleotide probe, or protein, in which case the probe can be, for example an antibody specific to that protein or an antigenic epitope of the protein.
In yet another embodiment, the invention provides a gene group, wherein the expression pattern of the group of genes provides diagnostic for a lung disease. The gene group comprises gene transcripts encoded by a gene group consisting of at least for example 5, 10, 15, 20, 25, 30, preferably at least 36, still more preferably 40, still more preferably 45, and still more preferably 46, 47, 48, 49, or all 50 of the genes selected from the group consisting of and identified by their GenBank identification numbers: NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U 93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; AB014576.1; BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20 of the 36 genes that overlap with the individual predictors and, for example, 5-9 of the non-overlapping genes and combinations thereof.
In another embodiment, the invention provides a group of about 30-180, preferably, a group of about 36-150 genes, still more preferably a group of about 36-100, and still more preferably a group of about 36-50 genes, the expression profile of which is diagnostic of lung cancer in individuals who smoke.
In one embodiment, the invention provides a group of genes the expression of which is decreased in an individual having lung cancer. In one embodiment, the group of genes comprises at least 5-10, 10-15, 15-20, 20-25 genes selected from the group consisting of NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_006545.1; NM_002437.1; NM_006286; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_001696; NM_005494 /// NM_058246; NM_006368; NM_002268 /// NM_032771; NM_006694; NM_004691; NM_012394; NM_021800; NM_016049; NM_138387; NM_024531; and NM_018509. One or more other genes can be added to the analysis mixtures in addition to these genes.
In another embodiment, the group of genes comprises genes selected from the group consisting of NM_014182.1; NM_001281.1; NM_024006.1; AF135421.1; L76200.1; NM_000346.1; BC008710.1; BC000423.2; BC008710.1; NM_007062; BC075839.1 /// BC073760.1; BC072436.1 /// BC004560.2; BC001016.2; BC005023.1; BC000360.2; BC007455.2; BC023528.2 /// BC047680.1; BC064957.1; BC008710.1; BC066329.1; BC023976.2; BC008591.2 /// BC050440.1 /// BC048096.1; and BC028912.1.
In yet another embodiment, the group of genes comprises genes selected from the group consisting of NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1.
In one embodiment, the invention provides a group of genes the expression of which is increased in an individual having lung cancer. In one embodiment, the group of genes comprises genes selected from the group consisting of NM_003335; NM_001319; NM_021145.1; NM_001003698 /// NM_001003699 ///; NM_002955; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_006534 /// NM_181659; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_000693; NM_033128; NM_020706; AI523613; and NM_014884.
In one embodiment, the group of genes comprises genes selected from the group consisting of NM_030757.1; R83000; AK021571.1; NM_17932.1; U85430.1; AI683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; BC061522.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1 ///; BC038443.1; Hs.288575 (UNIGENE ID); AF020591.1; BC002503.2; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; Hs.249591 (Unigene ID); Hs.286261 (Unigene ID); AF348514.1; BC066337.1 /// BC058736.1 /// BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000701.2; BC010067.2; Hs.156701 (Unigene ID); BC030619.2; U43965.1; Hs.438867 (Unigene ID); BC035025.2 /// BC050330.1; BC074852.2 /// BC074851.2; Hs.445885 (Unigene ID); AF365931.1; and AF257099.1.
In one embodiment, the group of genes comprises genes selected from the group consisting of BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1.
In another embodiment, the invention provides a method for diagnosing a lung disease comprising obtaining a nucleic acid sample from lung, airways or mouth of an individual exposed to an air pollutant, analyzing the gene transcript levels of one or more gene groups provided by the present invention in the sample, and comparing the expression pattern of the gene group in the sample to an expression pattern of the same gene group in an individual, who is exposed to similar air pollutant but not having lung disease, such as lung cancer or emphysema, wherein the difference in the expression pattern is indicative of the test individual having or being at high risk of developing a lung disease. The decreased expression of one or more of the genes, preferably all of the genes including the genes listed on Tables 1-4 as “down” when compared to a control, and/or increased expression of one or more genes, preferably all of the genes listed on Tables 1-4 as “up” when compared to an individual exposed to similar air pollutants who does not have a lung disease, is indicative of the person having a lung disease or being at high risk of developing a lung disease, preferably lung cancer, in the near future and needing frequent follow ups to allow early treatment of the disease.
In one preferred embodiment, the lung disease is lung cancer. In one embodiment, the air pollutant is cigarette smoke.
Alternatively, the diagnosis can separate the individuals, such as smokers, who are at lesser risk of developing lung diseases, such as lung cancer by analyzing the expression pattern of the gene groups of the invention provides a method of excluding individuals from invasive and frequent follow ups.
Accordingly, the invention provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from an individual who smokes and analyzing expression profile of the gene groups of the present invention, wherein an expression pattern of the gene group that deviates from that in a healthy age, race, and gender matched smoker, is indicative of an increased risk of developing a lung disease. Tables 1-4 indicate the expression pattern differences as either being down or up as compared to a control, which is an individual exposed to similar airway pollutant but not affected with a lung disease.
The invention also provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from a non-smoker individual and analyzing expression profile of the gene groups of the present invention, wherein an expression pattern of the gene group that deviates from that in a healthy age, race, and gender matched smoker, is indicative of an increased risk of developing a lung disease.
In one embodiment, the analysis is performed from a biological sample obtained from bronchial airways.
In one embodiment, the analysis is performed from a biological sample obtained from buccal mucosa.
In one embodiment, the analysis is performed using nucleic acids, preferably RNA, in the biological sample.
In one embodiment, the analysis is performed analyzing the amount of proteins encoded by the genes of the gene groups of the invention present in the sample.
In one embodiment the analysis is performed using DNA by analyzing the gene expression regulatory regions of the groups of genes of the present invention using nucleic acid polymorphisms, such as single nucleic acid polymorphisms or SNPs, wherein polymorphisms known to be associated with increased or decreased expression are used to indicate increased or decreased gene expression in the individual. For example, methylation patterns of the regulatory regions of these genes can be analyzed.
In one embodiment, the present invention provides a minimally invasive sample procurement method for obtaining airway epithelial cell RNA that can be analyzed by expression profiling of the groups of genes, for example, by array-based gene expression profiling. These methods can be used to diagnose individuals who are already affected with a lung disease, such as lung cancer, or who are at high risk of developing lung disease, such as lung cancer, as a consequence of being exposed to air pollutants. These methods can also be used to identify further patterns of gene expression that are diagnostic of lung disorders/diseases, for example, cancer or emphysema, and to identify subjects at risk for developing lung disorders.
The invention further provides a gene group microarray consisting of one or more of the gene groups provided by the invention, specifically intended for the diagnosis or prediction of lung disorders or determining susceptibility of an individual to lung disorders.
In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining a sample, nucleic acid or protein sample, from an individual to be diagnosed; and determining the expression of group of identified genes in said sample, wherein changed expression of such gene compared to the expression pattern of the same gene in a healthy individual with similar life style and environment is indicative of the individual having a disease of the lung.
In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining at least two samples, nucleic acid or protein samples, in at least one time interval from an individual to be diagnosed; and determining the expression of the group of identified genes in said sample, wherein changed expression of at least about for example 5, 10, 15, 20, 25, 30, preferably at least about 36, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, or 180 of such genes in the sample taken later in time compared to the sample taken earlier in time is diagnostic of a lung disease.
In one embodiment, the disease of the lung is selected from the group consisting of asthma, chronic bronchitis, emphysema, primary pulmonary hypertension, acute respiratory distress syndrome, hypersensitivity pneumonitis, eosinophilic pneumonia, persistent fungal infection, pulmonary fibrosis, systemic sclerosis, idiopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, and lung cancer, such as adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and benign neoplasm of the lung (e.g., bronchial adenomas and hamartomas).
In a particular embodiment, the nucleic acid sample is RNA.
In a preferred embodiment, the nucleic acid sample is obtained from an airway epithelial cell. In one embodiment, the airway epithelial cell is obtained from a bronchoscopy or buccal mucosal scraping.
In one embodiment, individual to be diagnosed is an individual who has been exposed to tobacco smoke, an individual who has smoked, or an individual who currently smokes.
The invention also provides an array, for example, a microarray for diagnosis of a disease of the lung having immobilized thereon a plurality of oligonucleotides which hybridize specifically to genes of the gene groups which are differentially expressed in airways exposed to air pollutants, such as cigarette smoke, and have or are at high risk of developing lung disease, as compared to those individuals who are exposed to similar air pollutants and airways which are not exposed to such pollutants. In one embodiment, the oligonucleotides hybridize specifically to one allelic form of one or more genes which are differentially expressed for a disease of the lung. In a particular embodiment, the differentially expressed genes are selected from the group consisting of the genes shown in tables 1-4; preferably the group of genes comprises genes selected from the Table 3. In one preferred embodiment, the group of genes comprises the group of at least 20 genes selected from Table 3 and additional 5-10 genes selected from Tables 1 and 2. In one preferred embodiment, at least about 10 genes are selected from Table 4.
Some aspects of the present invention also provide prognostic and diagnostic methods to assess lung disease risk caused by airway pollutants. The methods according to the present invention use a novel minimally invasive sample procurement method and gene expression-based tools for the diagnosis and prognosis of diseases of the lung, particularly diagnosis and prognosis of lung cancer.
We have shown that exposure of airways to pollutants such as cigarette smoke, causes a so-called “field defect”, which refers to gene expression changes in all the epithelial cells lining the airways from mouth mucosal epithelial lining through the bronchial epithelial cell lining to the lungs. Because of this field defect, it is now possible to detect changes, for example, pre-malignant and malignant changes resulting in diseases of the lung using cell samples isolated from epithelial cells obtained not only from the lung biopsies but also from other, more accessible, parts of the airways including bronchial or mouth epithelial cell samples.
Some aspects of the present invention are based on the finding that that there are different patterns of gene expression between smokers and non-smokers. The genes involved can be grouped into clusters of related genes that are reacting to the irritants or pollutants. We have found unique sets of expressed genes or gene expression patterns associated with pre-malignancy in the lung and lung cancer in smokers and non-smokers. All of these expression patterns constitute expression signatures that indicate operability and pathways of cellular function that can be used to guide decisions regarding prognosis, diagnosis and possible therapy. Epithelial cell gene expression profiles obtained from relatively accessible sites can thus provide important prognostic, diagnostic, and therapeutic information which can be applied to diagnose and treat lung disorders.
We have found that cigarette smoking induces xenobiotic and redox regulating genes as well as several oncogenes, and decreases expression of several tumor suppressor genes and genes that regulate airway inflammation. We have identified a subset of smokers, who respond differently to cigarette smoke and appear thus to be predisposed, for example, to its carcinogenic effects, which permits us to screen for individuals at risks of developing lung diseases.
Some aspects of the present invention are based on characterization of “airway transcriptomes” or a signature gene expression profiles of the airways and identification of changes in this transcriptome that are associated with epithelial exposure to pollutants, such as direct or indirect exposure to cigarette smoke, asbestos, and smog. These airway transcriptome gene expression profiles provide information on lung tissue function upon cessation from smoking, predisposition to lung cancer in non-smokers and smokers, and predisposition to other lung diseases. The airway transcriptome expression pattern can be obtained from a non-smoker, wherein deviations in the normal expression pattern are indicative of increased risk of lung diseases. The airway transcriptome expression pattern can also be obtained from a non-smoking subject exposed to air pollutants, wherein deviation in the expression pattern associated with normal response to the air pollutants is indicative of increased risk of developing lung disease.
Accordingly, in one embodiment, the invention provides an “airway transcriptome” the expression pattern of which is useful in prognostic, diagnostic and therapeutic applications as described herein. We have discovered the expression of 85 genes, corresponding to 97 probesets on the Affymetrix U133A Genechip array, having expression patterns that differ significantly between healthy smokers and healthy non-smokers. Examples of these expression patterns are shown in
In one embodiment, the invention provides distinct airway “expression clusters”, i.e., sub-transcriptomes, comprised of related genes among the 85 genes that can be quickly screened for diagnosis, prognosis or treatment purposes. In one embodiment, the invention provides an airway sub-transcriptome comprising mucin genes of the airway transcriptome. Examples of mucin genes include muc 5 subtypes A, B, and C. In another embodiment, the invention provides a sub-transcriptome comprising cell adhesion molecules of the airway transcriptome, such as carcinoembryonic antigen-related adhesion molecule 6 and claudin 10 encoding genes. In another embodiment, the invention provides a sub-transcriptome comprising detoxification related genes of the airway transcriptome. Examples of these genes include cytochrome P450 subfamily I (dioxin-inducible) encoding genes, NADPH dehydrogenase encoding genes. For example, upregulation of transcripts of cytochrome P450 subfamily I (dioxin-inducible) encoding genes.
In yet another embodiment, the invention provides a sub-transcriptome comprising immune system regulation associated genes of the airway transcriptome. Examples of immunoregulatory genes include small inducible cytokine subfamily D encoding genes.
In another embodiment, the invention provides a sub-transcriptome comprising metallothionein genes of the airway transcriptome. Examples of metallothionein genes include MTX G, X, and L encodinggenes.
In another embodiment, the subtranscriptome comprises protooncogenes and oncogenes such as RAB1-1A and CEACAM6. In another embodiment, the subtranscriptome includes tumor suppressor genes such as SLIT1, and SLIT2.
In one embodiment, the invention provides a lung cancer “diagnostic airway transcriptome” comprising 208 genes selected from the group consisting of group consisting of 208238_x_at-probeset; 216384_x_at-probeset; 217679_x_at-probeset; 216859_x_at-probeset; 211200_s_at-probeset; PDPK1; ADAM28; ACACB; ASMTL; ACVR2B; ADATI; ALMSI; ANK3; ANK3-; DARS; AFURS1; ATP8B1; ABCCI; BTF3; BRD4; CELSR2; CALM31 CAPZB; CAPZBI CFLAR; CTSS; CD24; CBX3; C21orf106; C6orf111; C6orf62; CHC1; DCLRE1C; EML2; EMS1; EPHB6-; EEF2; FGFR3; FLJ20288; FVT1; GGTLA4; GRP; GLUL; HDGF; Homo sapiens cDNA FLJ11452 fis, clone HEMBA1001435; Homo sapiens cDNA FLJ12005 fis, clone HEMBB1001565; Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450; Homo sapiens cDNA FLJ14090 fis, clone MAMMA1000264; Homo sapiens cDNA FLJ14253 fis, clone OVARC1001376; Homo sapiens fetal thymus prothymosin alpha mRNA, complete eds Homo sapiens fetal thymus prothymosin alpha mRNA; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_004726.1 (H. sapiens) leucine rich repeat (in FLU) interacting protein 1; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; 222282_at-probeset corresponding to Homo sapiens transcribed sequences; 21.5032_at-probeset corresponding to Homo sapiens transcribed sequences; 8181 1_at-probeset corresponding to Homo sapiens transcribed sequences; DKFZp547K1 113; ET; FLJ10534; FLJ10743; FLJ13171; FLJ14639; FLJ14675; FLJ20195; FLJ20686; FLJ20700; CG005; CG005; MGC5384; IMP-2; INADAL; INHBC; KIAA0379; KIAA0676; KIAA0779; KIAAI 193; KTNI; KLF5; LRRFIP1; MKRN4; MANIC1; MVK; MUC20; MPZL1; MYO1A; MRLC2; NFATC3; ODAG; PARVA; PASK; PIK3C2B; PGF; PKP4; PRKX; PRKY; PTPRF; PTMA; PTMA; PHTF2; RAB14; ARHGEF6; RIPX; REC8L1; RIOK3; SEMA3F; SRRM21 MGC709071 SMT3H2; SLC28A3; SAT; SFRSI 11 SOX2; THOC2; TRIM51 USP7; USP9X; USH1C; AF020591; ZNF13 I; ZNF160; ZNF264; 217414_x_at-probeset;; 217232_x_at-probeset;; ATF3; ASXL2; ARF4L; APG5L; ATP6V0B; BAGI; BTG2; COMT; CTSZ; CGI-128; C14orf87; CLDN3; CYR61; CKAP1; DAF; DAF; DSIPI; DKFZP564G2022; DNAJB9; DDOST; DUSP1; DUSP6; DKC1; EGRI; EIF4EL3; EXT2; GMPPB; GSN; GUKI; HSPA8; Homo sapiens PRO2275 mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_006442.2, polyadenylate binding protein-interacting protein 1; HAXI; DKFZP434K046; IMAGE3455200; HYOUI; IDN 3; JUNB; KRT8; KIAA0IO0; KIAA0102; APH-IA; LSM4; MAGED2; MRPS7; MOCS2; MNDA; NDUFA8; NNT; NFIL3; PWPI; NR4A2; NUDT4; ORMDL2; PDAP2; PPIH; PBX3; P4HA2; PPP1R15A; PRGII P2RX4; SUi1; SUi1; SUi1; RAB5C; ARHB; RNASE4; RNH; RNPC4; SEC23B; SERPINAI; SH3GLB1; SLC35B1.; SOX9; SOX9; STCH; SDHC; TINF2; TCF8; E2-EPF; FOS; JUN; ZFP36; ZNF500; and ZDHHC4.
Accordingly, the invention provides methods of diagnosing lung cancer in an individual comprising trucing a biological sample from the airways of the individual and analyzing the expression of at least 10 genes, preferably at least 50 genes, still more preferably at least 100 genes, still more preferably at least 150 genes, still more preferably at least 200 genes selected from genes of the diagnostic airway transcriptome, wherein deviation in the expression of at least one, preferably at least 5, 10, 20, 50, 100, 150, 200 genes as compared to a control group is indicative of lung cancer in the individual.
Deviation is preferably decrease of the transcription of at least one gene selected from the group consisting of of 208238_x_at -probeset; 216384_x_at-probeset; 217679_x_at-probeset; 216859_x_at-probeset; 211200_s_at-probeset; PDPK1; ADAM28; ACACB; ASMTL; ACVR2B; ADATI; ALMS1; ANK3; ANK3; DARS; AFURS1; ATP8B1; ABCCI; BTF3; BRD4; CELSR2; CALM31CAPZB; CAPZB1CFLAR; CTSS; CD24; CBX3; C21orf106; C6orf111; C6orf62; CHC1; DCLREIC; EML2; EMSI; EPHB6; EEF2; FGFR3; FLJ20288; FVT1; GGTLA4; GRP; GLUL; HDGF; Homo sapiens cDNA FLJ11452 fis, clone HEMBA1001435; Homo sapiens cDNA FLJ12005 fis, clone HEMBB1001565; Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450; Homo sapiens cDNA FLJ14090 fis, clone MAMMA1000264; Homo sapiens cDNA FLJ14253 fis, clone OVARC1001376; Homo sapiens fetal thymus prothymosin alpha mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_004726.1 (H. sapiens) leucine rich repeat (in FL1I) interacting protein 1; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; 222282_at-probeset corresponding to Homo sapiens transcribed sequences; 215032_at-probeset corresponding to Homo sapiens transcribed sequences; 81811_at-probeset corresponding to Homo sapiens transcribed sequences; DKFZp547K11 13; ET; FLJ10534; FLJ10743; FLJ13171; FLJ14639; FLJ14675; FLJ20195; FLJ20686; FLJ20700; CGOOS; CGOOS; MGC5384; IMP-2; INADL; INHBC; KIAA0379; KIAA0676; KIAA0779; KIAAI 193; KTN1; KLFS; LRRFIP1; MKRN4; MANIC1; MVK; MUC20; MPZLI; MYO1A; MRLC2; NFATC3; ODAG; PARVA; PASK; PIK3C2B; PGF; PKP4; PRKX; PRKY; PTPRF; PTMA; PTMA; PHTF2; RAB14; ARHGEF6; RIPX; REC8L1; RIOK3; SEMA3F; SRRM21 MGC709071 SMT3H2; SLC28A3; SAT; SFRS1 11 SOX2; THOC2; TRIM51 USP7; USP9X; USHIC; AF020591; ZNFI31; ZNF160; and ZNF264 genes.
Deviation is preferably increase of the expression of at least one gene selected from the group consisting of 217414_x_at-probeset; 217232_x_at-probeset; ATF3; ASXL2; ARF4L; APGSL; ATP6VOB; BAGI; BTG2; COMT; CTSZ; CGI-128; C14orf87; CLDN3; CYR61; CKAP1; DAF; DAF; DSIPI, DKFZP564G2022; DNAJB9; DDOST; DUSP1; DUSP6; DKC1; EGRI; EIF4EL3; EXT2; GMPPB; GSN; GUKI; HSPA8; Homo sapiens PR02275 mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_006442.2, polyadenylate binding protein-interacting protein 1; HAX1; DKFZP434K046; IMAGE3455200; HYOUI; IDN3; JUNB; KRT8; KIAAO1OO; KIAA0102; APH-IA; LSM4; MAGED2; MRPS7; MOCS2; MNDA; NDUFA8; NNT; NFIL3; PWPI; NR4A2; NUDT4; ORMDL2; PDAP2; PPIH; PBX3; P4HA2; PPPIRI5A; PRGII P2RX4; SUi1; SUi1; SUi1; RABSC; ARHB; RNASE4; RNH; RNPC4; SEC23B; SERPINAI; SH3GLB1; SLC35B1; SOX9; SOX9; STCH; SDRC; TINF2; TCF8; E2-EPF; FOS; JUN; ZFP36; ZNFS00; and ZDHHC4 genes.
The genes are referred to using their HUGO names or alternatively the probeset number on Affymetrix (Affymetrix, Inc. (U.S.), Santa Clara, Calif.) probesets.
In one embodiment, the invention provides methods of prognosis and diagnosis of lung diseases comprising obtaining a biological sample from a subject's airways, analyzing the level of expression of at least one gene of the airway transcriptome, comparing the level of expression of the at least one gene of at least one of the airway transcriptome to the level of expression in a control, wherein deviation in the level of expression in the sample from the control is indicative of increased risk of lung disease.
Preferably the analysis is performed using expression of at least two genes of the airway transcriptome, more preferably at least three genes, still more preferably at least four to 10 genes, still more preferably at least 10-20 genes, still more preferably at least 20-30, still more preferably at least 30-40, still more preferably at least 40-50, still more preferably at least 50-60, still more preferably at least 60-70, still more preferably at least 70-85 genes is analyzed.
In one preferred embodiment, the expression level of the genes of one or more of the sub-transcriptomes is analyzed. Preferably, gene expression of one or more genes belonging to at least two different sub-transcriptome sets is analyzed. Still more preferably, gene expression of at least one gene from at least three sub-transcriptome sets is analyzed. Still more preferably, gene expression of at least one gene from at least four sub-transcriptome sets is analyzed. Still more preferably, gene expression of at least one gene from at least five sub-transcriptome sets is analyzed.
The expression analysis according to the methods of the present invention can be performed using nucleic acids, particularly RNA, DNA or protein analysis.
The cell samples are preferably obtained from bronchial airways using, for example, endoscopic cytobrush in connection with a fiberoptic bronchoscopy. In one preferred embodiment, the cells are obtained from the individual's mouth buccal cells, using, for example, a scraping of the buccal mucosa. In one preferred embodiment, the invention provides a prognostic and/or diagnostic immunohistochemical approach, such as a dip-stick analysis, to determine risk of developing lung disease. Antibodies against at least one, preferably more proteins encoded by the genes of the airway transcriptome are either commercially available or can be produced using methods well know to one skilled in the art.
The invention further provides an airway transcriptone expression pattern of genes that correlate with time since cigarette discontinuance in former smokers, i.e., the expression of these genes in a healthy smoker returns to normal, or healthy non-smoker levels, after about two years from quitting smoking. These genes include: MAGF, GCLC, UTGIAI0, SLIT2, PECI, SLIT1, and TNFSF13. If the transcription of these genes has not returned to the level of a healthy non-smoker, as measured using the methods of the present invention, within a time period of about 1-5 years, preferably about 1.5-2.5 years, the individual with a remaining abnormal expression is at increased risk of developing a lung disease.
The invention further provides an airway transcriptome expression pattern of genes the expression of which remains abnormal after cessation from smoking. These genes include: CX3CL1, RNAHP, MT1X, MT1L, TU3A, HLF, CYFIP2, PLA2G10, HN1, GMDS, PLEKHB2, CEACAM6, ME1, and DPYSL3.
Accordingly, the invention provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from an individual who smokes and analyzing expression of at least one, preferably at least two, more preferably at least three, still more preferably at least four, still more preferably at least five, still more preferably at least six, seven, eight, and still more preferably at least nine genes of the normal airway transcriptome, wherein an expression pattern of the gene or genes that deviates from that in a healthy age, race, and gender matched smoker, is indicative of an increased risk of developing a lung disease.
The invention also provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from a non-smoker individual and analyzing expression of at least one, preferably at least two, more preferably at least three, still more preferably at least four, still more preferably at least five, still more preferably at least six, seven, eight, and still more preferably at least nine genes of the normal airway transcriptome, wherein an expression pattern of the gene or genes that deviates from that in a healthy age, race, and gender matched non-smoker, is indicative of an increased risk of developing a lung disease. Non-smoking individual whose expression pattern begins to resemble that of a smoker and at increased risk of developing a lung disease.
In one embodiment, the analysis is performed from a biological sample obtained from bronchial airways. In one embodiment, the analysis is performed from a biological sample obtained from buccal mucosa. In one embodiment, the analysis is performed using nucleic acids, preferably RNA, in the biological sample. In one embodiment, the analysis is performed analyzing the amount of proteins encoded by the genes of the airway transcriptome present in the sample.
In one embodiment the analysis is performed using DNA by analyzing the gene expression regulatory regions of the airway transcriptome genes using nucleic acid polymorphisms, such as single nucleic acid polymorphisms or SNPs, wherein polymorphisms known to be associated with increased or decreased expression are used to indicate increased or decreased gene expression in the individual.
In one embodiment, the present invention provides a method for determining whether a subject has or is at risk of developing a lung disorder, comprising (a) obtaining a biological sample comprising epithelial cells from a part of an airway of the subject separate from a lung of the subject; (b) assaying nucleic acid molecules derived from the biological sample to identify a level of gene expression in the biological sample; (c) processing the level of gene expression against a control to determine a deviation in the level of expression; and (d) based on the deviation in (c), determining that the subject has the lung cancer or is at risk of developing the lung disorder.
In one embodiment, the present invention provides a minimally invasive sample procurement method for obtaining airway epithelial cell RNA that can be analyzed by expression profiling, for example, by array-based gene expression profiling. These methods can be used to determine if airway epithelial cell gene expression profiles are affected by cigarette smoke and if these profiles differ in smokers with and without lung cancer. These methods can also be used to identify patterns of gene expression that are diagnostic of lung disorders/diseases, for example, cancer or emphysema, and to identify subjects at risk for developing lung disorders. All or a subset of the genes identified according to the methods described herein can be used to design an array, for example, a microarray, specifically intended for the diagnosis or prediction of lung disorders or susceptibility to lung disorders. The efficacy of such custom-designed arrays can be further tested, for example, in a large clinical trial of smokers.
In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining a sample, nucleic acid or protein sample, from an individual to be diagnosed; and determining the expression of one or more of the 85 identified genes in said sample, wherein changed expression of such gene compared to the expression pattern of the same gene in a healthy individual with similar life style and environment is indicative of the individual having a disease of the lung.
In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining at least two samples, nucleic acid or protein samples, in at least one time interval from an individual to be diagnosed; and determining the expression of one or more of the 85 identified genes in said samples, wherein changed expression of such gene or genes in the sample taken later in time compared to the sample taken earlier in time is diagnostic of a lung disease.
In one embodiment, the disease of the lung is selected from the group consisting of asthma, chronic bronchitis, emphysema, primary pulmonary hypertension, acute respiratory distress syndrome, hypersensitivity pneumonitis, eosinophilic pneumonia, persistent fungal infection, pulmonary fibrosis, systemic sclerosis, ideopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, and lung cancer, such as adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and benign neoplasms of the lung (e.g., bronchial adenomas and hamartomas). In a particular embodiment, the nucleic acid sample is RNA. In a preferred embodiment, the nucleic acid sample is obtained from an airway epithelial cell. In one embodiment, the airway epithelial cell is obtained from a bronchoscopy or buccal mucosal scraping. In one embodiment, individual to be diagnosed is an individual who has been exposed to tobacco smoke, an individual who has smoked, or an individual who smokes.
In a preferred embodiment of the method, the genes are selected from the group consisting of the genes shown in
The invention further relates to a method of obtaining a nucleic acid sample for use in expression analysis for a disease of the lung comprising obtaining an airway epithelial cell sample from an individual; and rendering the nucleic acid molecules in said cell sample available for hybridization. The invention also relates to a method of treating a disease of the lung comprising administering to an individual in need thereof an effective amount of an agent which increases the expression of a gene whose expression is decreased in said individual as compared with a normal individual.
The invention further relates to a method of treating a disease of the lung comprising administering to an individual in need thereof an effective amount of an agent, which changes the expression of a gene to that expression level seen in a healthy individual having the similar life style and environment, and a pharmaceutically acceptable carrier.
The invention also relates to a method of treating a disease of the lung comprising administering to an individual in need thereof an effective amount of an agent which increases the activity of an expression product of such gene whose activity is decreased in said individual as compared with a normal individual.
The invention also relates to a method of treating a disease of the lung comprising administering to an individual in need thereof an effective amount of an agent which decreases the activity of an expression product of such gene whose activity is increased in said individual as compared with a normal individual.
The invention also provides an array, for example, a microarray for diagnosis of a disease of the lung having immobilized thereon a plurality of oligonucleotides which hybridize specifically to one or more genes which are differentially expressed in airways exposed to air pollutants, such as cigarette smoke, and airways which are not exposed to such pollutants. In one embodiment, the oligonucleotides hybridize specifically to one allelic form of one or more genes which are differentially expressed for a disease of the lung. In a particular embodiment, the differentially expressed genes are selected from the group consisting of the genes shown in
In some aspects, prognostic and diagnostic methods of the present invention are based on the finding that deviation from the normal expression pattern in the airway transcriptome is indicative of abnormal response of the airway cells and thus predisposes the subject to diseases of the lung. Therefore, all the comparisons as provided in the methods are performed against a normal airway transcriptome of a “normal” or “healthy” individual exposed to the pollutant, as provided by this invention. Examples of these normal expression patterns of the genes belonging to the airway transcriptome of the present invention are provided in
In one embodiment, the invention provides a prognostic method for lung diseases comprising detecting gene expression changes in the cell adhesion regulating genes of the airway transcriptome, wherein decrease in the expression compared with a “normal” smoker expression pattern is indicative of an increased risk of developing a lung disease. Examples of cell adhesion regulation related genes include carcinoembryonic antigen-related adhesion molecule 6 and claudin 10 encoding genes. For example, an about at least 2-20 fold, preferably about at least 3 fold, still more preferably at least about 4 fold, still more preferably about at least 5 fold decrease in expression of carcinoembryonic antigen-related adhesion molecule 6 encoding gene is indicative of an increased risk of developing a lung disease. Also, for example, an about 2-20, preferably at least about, 3 fold, still more preferably at least about 4 fold, still more preferably at least about 5 fold decrease in the transcript level of claudin 10 encoding gene is indicative of an increased risk of developing a lung disease.
In one embodiment, the invention provides a prognostic method for lung diseases comprising detecting gene expression changes in the detoxification related genes of the airway transcriptome, wherein decrease in the expression compared with a “normal” smoker expression pattern is indicative of an increased risk of developing a lung disease. Examples of these genes include cytochrome P450 subfamily I (dioxin-inducible) encoding genes, NADPH dehydrogenase encoding genes. For example, upregulation of transcripts of cytochrome P450 subfamily I (dioxin-inducible) encoding genes of about 2-50 fold, preferably at least about, 5 fold, still more preferably about 10 fold, still more preferably at least about 15 fold, still more preferably at least about 20 fold, still more preferably at least about 30 fold, and downregulation of transcription of NADPH dehydrogenase encoding genes of about 2-20, preferably about at least 3 fold, still more preferably at least about 4 fold, still more preferably about at least 5 fold decrease compared to expression in a “normal” smoker is indicative of an increased risk of developing a lung disease.
In one embodiment, the invention provides a prognostic method for lung diseases comprising detecting gene expression changes in the immune system regulation associated genes of the airway transcriptome, wherein increase in the expression compared with a “normal” smoker expression pattern is indicative of an increased risk of developing a lung disease. Examples of immunoregulatory genes include small inducible cytokine subfamily D encoding genes. For example, about 1-10 fold difference in the expression of cytokine subfamily D encoding genes is indicative of increased risk of developing lung disease. Preferably, the difference in expression is least about 2 fold preferably about at least 3 fold, still more preferably at least about 4 fold, still more preferably about at least 5 fold decrease decrease in the expression of small inducible cytokine subfamily D encoding genes is indicative of an increased risk of developing a lung disease.
In one embodiment, the invention provides a prognostic method for lung diseases comprising detecting gene expression changes in the metalothionein regulation associated genes of the airway transcriptome, wherein decrease in the expression compared with a “normal” smoker is indicative of an increased risk of developing a lung disease. Examples of metalothionein regulation associated genes include MTX G, X, and L encoding genes. At least about 1.5-10 fold difference in the expression of these genes in indicative of increased risk of developing lung disease. For example, at least about 1.5 fold., still more preferably at least about 2 fold, still more preferably at least about 2.5 fold, still more preferably at least about 3 fold, still more preferably at least about 4 fold, still more preferably about at least. 5 fold increase in the expression of metalothionein regulation associated genes include MTX G, X, and L encoding genes indicative of an increased risk of developing a lung disease.
Prediction strength is a measure of the level of diagnostic confidence and varies on a continuous scale from 0 to 1 where 1 indicates a high degree of confidence.
The present invention is directed to gene/transcript groups and methods of using the expression profile of these gene/transcript groups in diagnosis and prognosis of lung diseases.
We provide a method that significantly increases the diagnostic accuracy of lung diseases, such as lung cancer. When one combines the gene expression analysis of the present invention with bronchoscopy, the diagnosis of lung cancer is dramatically better by detecting the cancer in an earlier stage than any other available method to date, and by providing far fewer false negatives and/or false positives than any other available method.
We have found a group of gene transcripts that we can use individually and in groups or subsets for enhanced diagnosis for lung diseases, such as lung cancer, using gene expression analysis. We provide detailed guidance on the increase and/or decrease of expression of these genes for diagnosis and prognosis of lung diseases, such as lung cancer.
One example of the gene transcript groups useful in the diagnostic/prognostic tests of the invention is set forth in Table 6. We have found that taking any group that has at least 20 of the Table 6 genes provides a much greater diagnostic capability than chance alone.
Preferably one would use more than 20 of these gene transcript, for example about 20-100 and any combination between, for example, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. Our preferred groups are the groups of 96 (Table 1), 84 (Table 2), 50 (Table 3), 36 (Table 4), 80 (Table 5), 535 (Table 6) and 20 (Table 7). In some instances, we have found that one can enhance the accuracy of the diagnosis by adding additional genes to any of these specific groups.
Naturally, following the teachings of the present invention, one may also include one or more of the genes and/or transcripts presented in Tables 1-7 into a kit or a system for a multicancer screening kit. For example, any one or more genes and or transcripts from Table 7 may be added as a lung cancer marker for a gene expression analysis.
When one uses these groups, the genes in the group are compared to a control or a control group. The control groups can be non-smokers, smokers, or former smokers. Preferably, one compares the gene transcripts or their expression product in the biological sample of an individual against a similar group, except that the members of the control groups do not have the lung disorder, such as emphysema or lung cancer. For example, comparing can be performed in the biological sample from a smoker against a control group of smokers who do not have lung cancer. When one compares the transcripts or expression products against the control for increased expression or decreased expression, which depends upon the particular gene and is set forth in the tables—not all the genes surveyed will show an increase or decrease. However, at least 50% of the genes surveyed must provide the described pattern. Greater reliability if obtained as the percent approaches 100%. Thus, in one embodiment, one wants at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99% of the genes surveyed to show the altered pattern indicative of lung disease, such as lung cancer, as set forth in the tables as shown below.
The presently described gene expression profile can also be used to screen for individuals who are susceptible for lung cancer. For example, a smoker, who is over a certain age, for example over 40 years old, or a smoker who has smoked, for example, a certain number of years, may wish to be screened for lung cancer. The gene expression analysis as described herein can provide an accurate very early diagnosis for lung cancer. This is particularly useful in diagnosis of lung cancer, because the earlier the cancer is detected, the better the survival rate is.
For example, when we analyzed the gene expression results, we found, that if one applies a less stringent threshold, the group of 80 genes as presented in Table 5 are part of the most frequently chosen genes across 1000 statistical test runs (see Examples below for more details regarding the statistical testing). Using random data, we have shown that no random gene shows up more than 67 times out of 1000. Using such a cutoff, the 535 genes of Table 6 in our data show up more than 67 times out of 1000. All the 80 genes in Table 5 form a subset of the 535 genes. Table 7 shows the top 20 genes which are subset of the 535 list. The direction of change in expression is shown using signal to noise ratio. A negative number in Tables 5, 6, and 7 means that expression of this gene or transcript is up in lung cancer samples. Positive number in Table 5, 6, and 7, indicates that the expression of this gene or transcript is down in lung cancer.
Accordingly, any combination of the genes and/or transcripts of Table 6 can be used. In one embodiment, any combination of at least 5-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100, 100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190, 190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260, 260-270, 270-280, 280-290, 290-300, 300-310, 310-320, 320-330, 330-340, 340-350, 350-360, 360-370, 370-380, 380-390, 390-400, 400-410, 410-420, 420-430, 430-440, 440-450, 450-460, 460-470, 470-480, 480-490, 490-500, 500-510, 510-520, 520-530, and up to about 535 genes selected from the group consisting of genes or transcripts as shown in the Table 6.
Table 7 provides 20 of the most frequently variably expressed genes in lung cancer when compared to samples without cancer. Accordingly, in one embodiment, any combination of about 3-5, 5-10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or all 20 genes and/or transcripts of Table 7, or any sub-combination thereof are used.
In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and which comprises probes that hybridize ranging from 1 to 96 and all combinations in between for example 5, 10, 15, 20, 25, 30, 35, at least about 36, at least to 40, at lest to 50, at least to 60, to at least 70, to at least 80, to at least 90, or all of the following 96 gene sequences: NM_003335; NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_001319; NM_006545.1; NM_021145.1; NM_002437.1; NM_006286; NM_001003698 /// NM_001003699 /// NM_002955; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_001696; NM_005494 /// NM_058246; NM_006534 /// NM_181659; NM_006368; NM_002268 /// NM_032771; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_006694; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_004691; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_012394; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_021800; NM_016049; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_138387; NM_024531; NM_000693; NM_018509; NM_033128; NM_020706; AI523613; and NM_014884
In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least, for example, 5, 10, 15, 20, 25, 30, 35, at least about 36, at least to 40, at least to 50, at least to 60, to at least 70, to at least 80, to all of the following 84 gene sequences: NM_030757.1; R83000; AK021571.1; NM_014182.1; NM_17932.1; U85430.1; AI683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; NM_001281.1; NM_024006.1; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; AF135421.1; BC061522.1; L76200.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1 /// BC038443.1; NM_000346.1; BC008710.1; Hs.288575 (UNIGENE ID); AF020591.1; BC000423.2; BC002503.2; BC008710.1; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; NM_007062; Hs.249591 (Unigene ID); BC075839.1 /// BC073760.1; BC072436.1 /// BC004560.2; BC001016.2; Hs.286261 (Unigene ID); AF348514.1; BC005023.1; BC066337.1 /// BC058736.1 /// BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000360.2; BC007455.2; BC000701.2; BC010067.2; BC023528.2 /// BC047680.1; BC064957.1; Hs.156701 (Unigene ID); BC030619.2; BC008710.1; U43965.1; BC066329.1; Hs.438867 (Unigene ID); BC035025.2 /// BC050330.1; BC023976.2; BC074852.2 /// BC074851.2; Hs.445885 (Unigene ID); BC008591.2 /// BC050440.1 ///; BC048096.1; AF365931.1; AF257099.1; and BC028912.1.
In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least, for example 5, 10, 15, 20, 25, 30, preferably at least about 36, still more preferably at least to 40, still more preferably at lest to 45, still more preferably all of the following 50 gene sequences, although it can include any and all members, for example, 20, 21, 22, up to and including 36: NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; AB014576.1; BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20-30, 30-40, of the 50 genes that overlap with the individual predictor genes identified in the analysis using the t-test, and, for example, 5-9 of the non-overlapping genes, identified using the t-test analysis as individual predictor genes, and combinations thereof.
In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least for example 5, 10, 15, 20, preferably at least about 25, still more preferably at least to 30, still more preferably all of the following 36 gene sequences: NM_007062.1; NM_001281.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; NM_002268 /// NM_032771; NM_007048 /// NM_194441; NM_006694; U85430.1; NM_004691; AB014576.1; BF218804; BE467941; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_021971.1; NM_014128.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20 of the 36 genes that overlap with the individual predictors and, for example, 5-9 of the non-overlapping genes, and combinations thereof.
The expression of the gene groups in an individual sample can be analyzed using any probe specific to the nucleic acid sequences or protein product sequences encoded by the gene group members. For example, in one embodiment, a probe set useful in the methods of the present invention is selected from the nucleic acid probes of between 10-15, 15-20, 20-180, preferably between 30-180, still more preferably between 36-96, still more preferably between 36-84, still more preferably between 36-50 probes, included in the Affymetrix Inc. gene chip of the Human Genome U133 Set and identified as probe ID Nos: 208082_x_at, 214800_x_at, 215208_x_at, 218556_at, 207730_x_at, 210556_at, 217679_x_at, 202901_x_at, 213939_s_at, 208137_x_at, 214705_at, 215001_s_at, 218155_x_at, 215604_x_at, 212297_at, 201804_x_at, 217949_s_at, 215179_x_at, 211316_x_at, 217653_x_at, 266_s_at, 204718_at, 211916_s_at, 215032_at, 219920_s_at, 211996_s_at, 200075_s_at, 214753_at, 204102_s_at, 202419_at, 214715_x_at, 216859_x_at, 215529_x_at, 202936_s_at, 212130_x_at, 215204_at, 218735_s_at, 200078_s_at, 203455_s_at, 212227_x_at, 222282_at, 219678_x_at, 208268_at, 221899_at, 213721_at, 214718_at, 201608_s_at, 205684_s_at, 209008_x_at, 200825_s_at, 218160_at, 57739_at, 211921_x_at, 218074_at, 200914_x_at, 216384_x_at, 214594_x_at, 222122_s_at, 204060_s_at, 215314_at, 208238_x_at, 210705_s_at, 211184_s_at, 215418_at, 209393_s_at, 210101_x_at, 212052_s_at, 215011_at, 221932_s_at, 201239_s_at, 215553_x_at, 213351_s_at, 202021_x_at, 209442_x_at, 210131_x_at, 217713_x_at, 214707_x_at, 203272_s_at, 206279_at, 214912_at, 201729_s_at, 205917_at, 200772_x_at, 202842_s_at, 203588_s_at, 209703_x_at, 217313_at, 217588_at, 214153_at, 222155_s_at, 203704_s_at, 220934_s_at, 206929_s_at, 220459_at, 215645_at, 217336_at, 203301_s_at, 207283_at, 222168_at, 222272_x_at, 219290_x_at, 204119_s_at, 215387_x_at, 222358_x_at, 205010_at, 1316_at, 216187_x_at, 208678_at, 222310_at, 210434_x_at, 220242_x_at, 207287_at, 207953_at, 209015_s_at, 221759_at, 220856_x_at, 200654_at, 220071_x_at, 216745_x_at, 218976_at, 214833_at, 202004_x_at, 209653_at, 210858_x_at, 212041_at, 221294_at, 207020_at, 204461_x_at, 205367_at, 219203_at, 215067_x_at, 212517_at, 220215_at, 201923_at, 215609_at, 207984_s_at, 215373_x_at, 216110_x_at, 215600_x_at, 216922_x_at, 215892_at, 201530_x_at, 217371_s_at, 222231_s_at, 218265_at, 201537_s_at, 221616_s_at, 213106_at, 215336_at, 209770_at, 209061_at, 202573_at, 207064_s_at, 64371_at, 219977_at, 218617_at, 214902_x_at, 207436_x_at, 215659_at, 204216_s_at, 214763_at, 200877_at, 218425_at, 203246_s_at, 203466_at, 204247_s_at, 216012_at, 211328_x_at, 218336_at, 209746_s_at, 214722_at, 214599_at, 220113_x_at, 213212_x_at, 217671_at, 207365_x_at, 218067_s_at, 205238_at, 209432_s_at, and 213919_at. In one preferred embodiment, one can use at least, for example, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, 110, 120, 130, 140, 150, 160, or 170 of the 180 genes that overlap with the individual predictors genes and, for example, 5-9 of the non-overlapping genes and combinations thereof
Sequences for the Affymetrix probes are provided in the Appendix to the specification, all the pages of which are herein incorporated by reference in their entirety.
One can analyze the expression data to identify expression patters associated with any lung disease that is caused by exposure to air pollutants, such as cigarette smoke, asbestos or any other lung disease. For example, the analysis can be performed as follows. One first scans a gene chip or mixture of beads comprising probes that are hybridized with a study group samples. For example, one can use samples of non-smokers and smokers, non-asbestos exposed individuals and asbestos-exposed individuals, non-smog exposed individuals and smog-exposed individuals, smokers without a lung disease and smokers with lung disease, to obtain the differentially expressed gene groups between individuals with no lung disease and individuals with lung disease. One must, of course select appropriate groups, wherein only one air pollutant can be selected as a variable. So, for example, one can compare non-smokers exposed to asbestos but not smog and non-smokers not exposed to asbestos or smog.
The obtained expression analysis, such as microarray or microbead raw data consists of signal strength and detection p-value. One normalizes or scales the data, and filters the poor quality chips/bead sets based on images of the expression data, control probes, and histograms. One also filters contaminated specimens which contain non-epithelial cells. Lastly, one filters the genes of importance using detection p-value. This results in identification of transcripts present in normal airways (normal airway transcriptome). Variability and multiple regression analysis can be used. This also results in identification of effects of smoking on airway epithelial cell transcription. For this analysis, one can use T-test and Pearson correlation analysis. One can also identify a group or a set of transcripts that are differentially expressed in samples with lung disease, such as lung cancer and samples without cancer. This analysis was performed using class prediction models.
For analysis of the data, one can use, for example, a weighted voting method. The weighted voting method ranks, and gives a weight “p” to all genes by the signal to noise ration of gene expression between two classes: P=mean(class 1)−mean(class 2)/sd(class 1)=sd(class 2). Committees of variable sizes of the top ranked genes are used to evaluate test samples, but genes with more significant p-values can be more heavily weighed. Each committee genes in test sample votes for one class or the other, based on how close that gene expression level is to the class 1 mean or the class 2 mean. V(gene A)=P(gene A), i.e. level of expression in test sample less the average of the mean expression values in the two classes. Votes for each class are tallied and the winning class is determined along with prediction strength as PS=Vwin−Vlose/Vwin+Vlose. Finally, the accuracy can be validated using cross-validation +/−independent samples.
Table 1 shows 96 genes that were identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer. In one embodiment, the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used. Sequences for the Affymetrix probes are provided in the Appendix.
Table 2 shows one preferred 84 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer. These genes were identified using traditional Student's t-test analysis.
In one embodiment, the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.
sapiens]
Homo sapiens
Homo sapiens cDNA
Homo sapiens
Homo sapiens fetal
Homo sapiens cDNA
Homo sapiens
sapiens]
Homo sapiens cDNA
Table 3 shows one preferred 50 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer.
This gene group was identified using the GenePattern server from the Broad Institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used.
In one embodiment, the exemplary probes shown in the column “ Affymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.
Table 4 shows one preferred 36 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer.
In one embodiment, the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.
Homo sapiens cDNA
Homo sapiens 10q21
In one embodiment, the gene group of the present invention comprises at least, for example, 5, 10, 15, 20, 25, 30, more preferably at least 36, still more preferably at least about 40, still more preferably at least about 50, still more preferably at least about 60, still more preferably at least about 70, still more preferably at least about 80, still more preferably at least about 86, still more preferably at least about 90, still more preferably at least about 96 of the genes as shown in Tables 1-4.
In one preferred embodiment, the gene group comprises 36-180 genes selected from the group consisting of the genes listed in Tables 1-4.
In one embodiment, the invention provides group of genes the expression of which is lower in individuals with cancer.
Accordingly, in one embodiment, the invention provides of a group of genes useful in diagnosing lung diseases, wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-30, still more preferably at least about 30-40, still more preferably at least about 40-50, still more preferably at least about 50-60, still more preferably at least about 60-70, still more preferably about 72 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 1): NM_003335; NM_001319; NM_021145.1; NM_001003698 /// NM_001003699 ///; NM_002955; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_006534 /// NM_181659; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_000693; NM_033128; NM_020706; AI523613; and NM_014884.
In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-30, still more preferably at least about 30-40, still more preferably at least about 40-50, still more preferably at least about 50-60, still more preferably about 63 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 2): NM_030757.1; R83000; AK021571.1; NM_17932.1; U85430.1; AI683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; BC061522.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1 ///; BC038443.1; Hs.288575 (UNIGENE ID); AF020591.1; BC002503.2; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; Hs.249591 (Unigene ID); Hs.286261 (Unigene ID); AF348514.1; BC066337.1 /// BC058736.1 /// BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000701.2; BC010067.2; Hs.156701 (Unigene ID); BC030619.2; U43965.1; Hs.438867 (Unigene ID); BC035025.2 /// BC050330.1; BC074852.2 /// BC074851.2; Hs.445885 (Unigene ID); AF365931.1; and AF257099.1
In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still more preferably about 25 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 3): BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1.
In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least to 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still more preferably about 25 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 1): NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_006545.1; NM_002437.1; NM_006286; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_001696; NM_005494 /// NM_058246; NM_006368; NM_002268 /// NM_032771; NM_006694; NM_004691; NM_012394; NM_021800; NM_016049; NM_138387; NM_024531; and NM_018509.
In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least to 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-23, still more preferably about 23 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 2): NM_014182.1; NM_001281.1; NM_024006.1; AF135421.1; L76200.1; NM_000346.1; BC008710.1; BC000423.2; BC008710.1; NM_007062; BC075839.1 /// BC073760.1; BC072436.1 /// BC004560.2; BC001016.2; BC005023.1; BC000360.2; BC007455.2; BC023528.2 /// BC047680.1; BC064957.1; BC008710.1; BC066329.1; BC023976.2; BC008591.2 /// BC050440.1 /// BC048096.1; and BC028912.1.
In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least to 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still more preferably about 25 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 3): NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1.
In one embodiment, the invention provides a method of diagnosing lung disease comprising the steps of measuring the expression profile of a gene group in an individual suspected of being affected or being at high risk of a lung disease (i.e. test individual), and comparing the expression profile (i.e. control profile) to an expression profile of an individual without the lung disease who has also been exposed to similar air pollutant than the test individual (i.e. control individual), wherein differences in the expression of genes when compared between the afore mentioned test individual and control individual of at least 10, more preferably at least 20, still more preferably at least 30, still more preferably at least 36, still more preferably between 36-180, still more preferably between 36-96, still more preferably between 36-84, still more preferably between 36-50, is indicative of the test individual being affected with a lung disease. Groups of about 36 genes as shown in table 4, about 50 genes as shown in table 3, about 84 genes as shown in table 2 and about 96 genes as shown in table 1 are preferred. The different gene groups can also be combined, so that the test individual can be screened for all, three, two, or just one group as shown in tables 1-4.
For example, if the expression profile of a test individual exposed to cigarette smoke is compared to the expression profile of the 50 genes shown in table 3, using the Affymetrix inc probe set on a gene chip as shown in table 3, the expression profile that is similar to the one shown in
The group of 50 genes was identified using the GenePattern server from the Broad Institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used. GenePattern is available through the World Wide Wed at location broad.mit.edu/cancer/software/genepattern. This program allows analysis of data in groups rather than as individual genes. Thus, in one preferred embodiment, the expression of substantially all 50 genes of Table 3, are analyzed together. The expression profile of lower that normal expression of genes selected from the group consisting of BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1, and the gene expression profile of higher than normal expression of genes selected from the group consisting of NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1, is indicative of the individual having or being at high risk of developing lung disease, such as lung cancer. In one preferred embodiment, the expression pattern of all the genes in the Table 3 is analyzed. In one embodiment, in addition to analyzing the group of predictor genes of Table 3, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10-15, 15-20, 20-30, or more of the individual predictor genes identified using the t-test analysis are analyzed. Any combination of, for example, 5-10 or more of the group predictor genes and 5-10, or more of the individual genes can also be used.
The term “expression profile” as used herein, refers to the amount of the gene product of each of the analyzed individual genes in the sample. The “expression profile” is like a signature expression map, like the one shown for each individual in
The term “lung disease”, as used herein, refers to disorders including, but not limited to, asthma, chronic bronchitis, emphysema, bronchietasis, primary pulmonary hypertension and acute respiratory distress syndrome. The methods described herein may also be used to diagnose or treat lung disorders that involve the immune system including, hypersensitivity pneumonitis, eosinophilic pneumonias, and persistent fungal infections, pulmonary fibrosis, systemic sclerosis, idiopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, cancers of the lung such as adenocarcinoma, squamous cell carcinoma, small cell and large cell carcinomas, and benign neoplasm of the lung including bronchial adenomas and hamartomas. In one preferred embodiment, the lung disease is lung cancer.
The biological samples useful according to the present invention include, but are not limited to tissue samples, cell samples, and excretion samples, such as sputum or saliva, of the airways. The samples useful for the analysis methods according to the present invention can be taken from the mouth, the bronchial airways, and the lungs.
The term “air pollutants”, as used herein, refers to any air impurities or environmental airway stress inducing agents, such as cigarette smoke, cigar smoke, smog, asbestos, and other air pollutants that have suspected or proven association to lung diseases.
The term “individual”, as used herein, preferably refers to human However, the methods are not limited to humans, and a skilled artisan can use the diagnostic/prognostic gene groupings of the present invention in, for example, laboratory test animals, preferably animals that have lungs, such as non-human primates, murine species, including, but not limited to rats and mice, dogs, sheep, pig, guinea pigs, and other model animals
The term “field defect” as used throughout the specification means that the transcription pattern of epithelial cells lining the entire airway including the mouth buccal mucosa, airways, and lung tissue changes in response to airway pollutants. Therefore, the present invention provides methods to identify epithelial cell gene expression patterns that are associated with diseases and disorders of the lung.
The phrase “altered expression” as used herein, refers to either increased or decreased expression in an individual exposed to air pollutant, such as a smoker, with cancer when compared to an expression pattern of the lung cells from an individual exposed to similar air pollutant, such as smoker, who does not have cancer. Tables 1 and 2 show the preferred expression pattern changes of the invention. The terms “up” and “down” in the tables refer to the amount of expression in a smoker with cancer to the amount of expression in a smoker without cancer Similar expression pattern changes are likely associated with development of cancer in individuals who have been exposed to other airway pollutants.
In one embodiment, the group of genes the expression of which is analyzed in diagnosis and/or prognosis of lung cancer are selected from the group of 80 genes as shown in Table 5. Any combination of genes can be selected from the 80 genes. In one embodiment, the combination of 20 genes shown in Table 7 is selected. In one embodiment, a combination of genes from Table 6 is selected.
One can use the above tables to correlate or compare the expression of the transcript to the expression of the gene product. Increased expression of the transcript as shown in the table corresponds to increased expression of the gene product. Similarly, decreased expression of the transcript as shown in the table corresponds to decreased expression of the gene product
The analysis of the gene expression of one or more genes and/or transcripts of the groups or their subgroups of the present invention can be performed using any gene expression method known to one skilled in the art. Such methods include, but are not limited to expression analysis using nucleic acid chips (e.g. Affymetrix chips) and quantitative RT-PCR based methods using, for example real-time detection of the transcripts. Analysis of transcript levels according to the present invention can be made using total or messenger RNA or proteins encoded by the genes identified in the diagnostic gene groups of the present invention as a starting material. In the preferred embodiment the analysis is an immunohistochemical analysis with an antibody directed against proteins comprising at least about 10-20, 20-30, preferably at least 36,_at least 36-50, 50, about 50-60, 60-70, 70-80, 80-90, 96, 100-180, 180-200, 200-250, 250-300, 300-350, 350-400, 400-450, 450-500, 500-535 proteins encoded by the genes and/or transcripts as shown in Tables 1-7.
The methods of analyzing transcript levels of the gene groups in an individual include Northern-blot hybridization, ribonuclease protection assay, and reverse transcriptase polymerase chain reaction (RT-PCR) based methods. The different RT-PCR based techniques are the most suitable quantification method for diagnostic purposes of the present invention, because they are very sensitive and thus require only a small sample size which is desirable for a diagnostic test. A number of quantitative RT-PCR based methods have been described and are useful in measuring the amount of transcripts according to the present invention. These methods include RNA quantification using PCR and complementary DNA (cDNA) arrays (Shalon et al., Genome Research 6(7):639-45, 1996; Bernard et al., Nucleic Acids Research 24(8):1435-42, 1996), real competitive PCR using a MALDI-TOF Mass spectrometry based approach (Ding et al, PNAS, 100: 3059-64, 2003), solid-phase mini-sequencing technique, which is based upon a primer extension reaction (U.S. Pat. No. 6,013,431, Suomalainen et al. Mol. Biotechnol. June; 15(2):123-31, 2000), ion-pair high-performance liquid chromatography (Doris et al. J. Chromatogr. A May 8;806(1):47-60, 1998), and 5′ nuclease assay or real-time RT-PCR (Holland et al. Proc Natl Acad Sci USA 88: 7276-7280, 1991).
Methods using RT-PCR and internal standards differing by length or restriction endonuclease site from the desired target sequence allowing comparison of the standard with the target using gel electrophoretic separation methods followed by densitometric quantification of the target have also been developed and can be used to detect the amount of the transcripts according to the present invention (see, e.g., U.S. Pat. Nos. 5,876,978; 5,643,765; and 5,639,606.
The samples are preferably obtained from bronchial airways using, for example, endoscopic cytobrush in connection with a fiber optic bronchoscopy. In one embodiment, the cells are obtained from the individual's mouth buccal cells, using, for example, a scraping of the buccal mucosa.
In one preferred embodiment, the invention provides a prognostic and/or diagnostic immunohistochemical approach, such as a dip-stick analysis, to determine risk of developing lung disease. Antibodies against proteins, or antigenic epitopes thereof, that are encoded by the group of genes of the present invention, are either commercially available or can be produced using methods well know to one skilled in the art.
Some aspects of the present invention also provide prognostic, diagnostic, and therapeutic tools for the disorders of lung, particularly, lung cancer. The invention is based on the identification of a “field defect” phenomenon and specific expression patterns related to airway epithelial cell exposure to pollutants, such as cigarette smoke. The airway expression patterns of the present invention can be analyzed using nucleic acids and/or proteins from a biological sample of the airways.
For example, lung cancer involves histopathological and molecular progression from normal to premalignant to cancer. Gene expression arrays of lung tumors have been used to characterize expression profiles of lung cancers, and to show the progression of molecular changes from non-malignant lung tissue to lung cancer. However, for the screening and early diagnostic purpose, it is not practicable to obtain samples from the lungs. Therefore, the present invention provides for the first time, a method of obtaining cells from other parts of the airways to identify the epithelial gene expression pattern in an individual.
The ability to determine which individuals have molecular changes in their airway epithelial cells and how these changes relate to a lung disorder, such as premalignant and malignant changes is a significant improvement for determining risk and for diagnosing a lung disorder such as cancer at a stage when treatment can be more effective, thus reducing the mortality and morbidity rates of lung cancer. The ease with which airway epithelial cells can be obtained, such as bronchoscopy and buccal mucosal scrapings, shows that this approach has wide clinical applicability and is a useful tool in a standard clinical screening for the large number of subjects at risk for developing disorders of the lung.
Lung disorders which may be diagnosed or treated by methods described herein include, but are not limited to, asthma, chronic bronchitis, emphysema, bronchietasis, primary pulmonary hypertension and acute respiratory distress syndrome. The methods described herein may also be used to diagnose or treat lung disorders that involve the immune system including, hypersensitivity pneumonitis, eosinophilic pneumonias, and persistent fungal infections, pulmonary fibrosis, systemic sclerosis, ideopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, cancers of the lung such as adenocarcinoma, squamous cell carcinoma, small cell and large cell carcinomas, and benign neoplasms of the lung including bronchial adenomas and hamartomas.
The biological samples useful according to the present invention include, but are not limited to tissue samples, cell samples, and excretion samples, such as sputum or saliva, of the airways. The samples useful for the analysis methods according to the present invention can be taken from the mouth, the bronchial airways, and the lungs.
In one embodiment, the invention provides an “airway transcriptome” the expression pattern of which is useful in prognostic, diagnostic and therapeutic applications as described herein. The airway transcriptome of the present invention comprises 85 genes the expression of which differs significantly between healthy smokers and healthy non-smokers. The airway transcriptome according to the present invention comprises 85 genes, corresponding to 97 probesets, as a number of genes are represented by more than one probeset on the affymetrix array, identified from the about 7100 probesets the expression of which was statistically analyzed using epithelial cell RNA samples from smokers and non-smokers. Therefore, the invention also provides proteins that are encoded by the 85 genes. The 85 identified airway transcriptome genes are listed on the following
Homo sapiens RNA helicase-related protein (Unigene/Hs. 8765)
Homo sapenes subhi-repeat protein (MutDB at
Homo sapiens NAD kinase (GenBank ID gi: 20070325)
The invention further provides a lung cancer diagnostic airway transcriptome comprising at least 208 genes that are differentially expressed between smokers with lung cancer and smokers witout lung cancer. The genes identified as being part of the diagnostic airway transcriptome are 208238_x_at-probeset; 216384x_at-probeset; 217679_x_at-probeset; 216859_x_at-probeset; 211200_s_at-probeset; PDPKI; ADAM28; ACACB; ASMTL; ACVR2B; ADAT1; ALMS1; ANK3; ANK3; OARS; AFURS1; ATP8B1; ABCC1; BTF3; BRD4; CELSR2; CALM31 CAPZB; CAPZB1 CFLAR; CTSS; CD24; CBX3; C21orf106; C6orf111; C6orf62; CHC1; DCLRE1C; EML2; EMS 1 EPHB6; EEF2; FGFR3; FLJ20288; FVT1; GGTLA4; GRP; GLUL; HDGF; Homo sapiens cDNA FLJ1 1452 fis, clone HEMBA1001435; Homo sapiens cDNA FLJ12005 fis, clone HEMBB1001565; Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450; Homo sapiens cDNA FLJ14090 fis, clone MAMMA1000264; Homo sapiens cDNA FLJ14253 fis, clone OVARC1001376; Homo sapiens fetal thymus prothymosin alpha mRNA, complete eds Homo sapiens fetal thymus prothymosin alpha mRNA; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_004726.1 (H. sapiens) leucine rich repeat (in FL1I) interacting protein 1; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; 222282_at-probeset corresponding to Homo sapiens transcribed sequences; 215032_at-probeset corresponding to Homo sapiens transcribed sequences; 81811_at-probeset corresponding to Homo sapiens transcribed sequences; OKFp4547K1 113; ET; FLJ10534; FLJ10743; FLJ13171; FLJ14639; FLJ14675; FLJ20195; FLJ20686; FLJ20700; CG005; CG005; MGC5384; IMP-2; INAOL; INHBC; KIAA0379; KIA A0676; KIAA0779; KIAA1193; KTNI; KLF5; LRRFIP1; MKRN4; MAN1C1; MVK; MUC20; MPZL1; MYO1A; MRLC2; NFATC3; OOAG; PARVA; PASK; PIK3C2B; PGF; PKP4; PRKX; PRKY; PTPRF; PTMA; PTMA; PHTF2; RAB14; ARHGEF6; RIPX; REC8L1; RIOK3; SEMA3F; SRRM21 MGC709071 SMT3H2; SLC28A3; SAT; SFRS111 SOX2; THOC2; TRIM51 USP7; USP9X; USHIC; AF020591; ZNF131; ZNF160; ZNF264; 217414_x_at-probeset;; 217232_x_at-probeset;; ATF3; ASXL2; ARF4L; APG5L; ATP6V0B; BAG1; BTG2; COMT; CTSZ; CGI-128; C14orf87; CLDN3; CYR61; CKAP1; OAF; OAF; OSIP1; OKFZP564G2022; ONAJB9; OOOST; OUSP1; DUSP6; DKC1; EGR1; EIF4EL3; EXT2; GMPPB; GSN; GUK1; HSPA8; Homo sapiens PRO2275 mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_006442.2, polyadenylate binding protein-interacting protein 1; HAX1; DKFZP434KO46; IMAGE3455200, HYOUI; IDN3; JUNB; KRT8; KIAA01O0; KIAA0102; APH-1A; LSM4; MAGED2; MRPS7; MOCS2; MNDA; NOUFA8; NNT; NFIL3; PWP1; NR4A2; NUDT4; ORMOL2; POAP2; PPIH; PBX3; P4HA2; PPP1R15A; PRGII P2RX4; SUi1; SUi1; SUi1; RABSC; ARHB; RNASE4; RNH; RNPC4; SEC23B; SERPINA1; SH3GLB1; SLC35B1; SOX9; SOX9; STCH; SDHC; TINF2; TCF8; E2-EPF; FOS; JUN; ZFP36; ZNF500; and ZDHHC4.
Deviation in the expression compared to control group can be increased expression or decreased expression of one or more of the 208 genes. Preferably, downregulation of expression of at least one, preferably at least 10, 15, 25, 30, 50, 60, 75, 80, 90, 100, 110, or all of the 121 genes consisting of 208238_x_at-probeset; 216384_x_at-probeset; 217679_x_at-probeset; 216859_x_at-probeset; 211200_s_at-probeset; PDPK1 ADAM28; ACACB; ASMTL; ACVR2B; ADAT1; ALMS1; ANK3; ANK3; OARS; AFURSI; ATP8B1; ABCC1; BTF3; BRD4; CELSR2; CALM31 CAPZB; CAPZB1 CFLAR; CTSS; CD24; CBX3; C21orf106; C6orf111; C6orf62; CHC1; DCLREIC; EMIL2; EMS1, EPHB6; EEF2; FGFR3; FLJ20288; FVT1; GGTLA4; GRP; GLUL HDGF; Homo sapiens cDNA FLJI 1452 fis, clone HEMBA1001435; Homo sapiens cDNA FLJ12005 fis, cloneHEMBB1001565; Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450; Homo sapiens cDNA FLJ14090 fis, clone MAMMA1000264; Homo sapiens cDNA FLJ14253 fis, clone OVARC1001376; Homo sapiens fetal thymus prothymosin alpha mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_004726.1 (H. sapiens) leucine rich repeat (in FLU) interacting protein 1; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489; 222282_at-probeset corresponding to Homo sapiens transcribed sequences; 215032_at-probeset corresponding to Homo sapiens transcribed sequences; 81811_at-probeset corresponding to Homo sapiens transcribed sequences; DKFZp547K1 113; ET; FLJ10534;FLJ10743;FLJ13171;FLJ14639;FLJ14675;FLJ20195;FLJ20686;FLJ20700; CG005; CG005; MGC5384; IMP-2; INADL; INHBC; KIAA0379; KIAA0676; K1AA0779; KIAA1193; KTN1; KLF5; LRRFIP1; MKRN4; MAN1C1; MVK; MUC20; MPZL1; MYO1A; MRLC2; NFATC3; ODAG; PARVA; PASK; PIK3C2B; PGF; PKP4; PRKX; PRKY; PTPRF; PTMA; PTMA; PHTF2; RAB14; ARHGEF6; RIPX; REC8L1; RIOK3; SEMA3F; SRRM21 MGC709071 SMT3H2; SLC28A3; SAT; SFRS111 SOX2; THOC2; TRIM51USP7; USP9X; USH1C; AF020591; ZNF131; ZNF160; and ZNF264, when compared to a control group is indicative of lung cancer.
Preferably increase, or up-regulation of expression of at least one, preferably at least 10, 15, 25, 30, 50, 60, 75, 80, or all of the 87 genes consisting of of217414_x_at-probeset;; 217232_x_at-probeset;;ATF3; ASXL2; ARF4L; APG5L; ATP6VOB; BAG1; BTG2; COMT; CTSZ; CGI-128; C14orf87; CLDN3; CYR61; CKAP1; DAF; DAF; DSIPI; DKFZP564G2022; DNAJB9; DDOST; DUSP1; DUSP6; DKC1; EGR1; EIF4EL3; EXT2; GMPPB; GSN; GUK1; HSPA8; Homo sapiens PRO2275 mRNA, complete eds; Homo sapiens transcribed sequence with strong similarity to protein ref:NP_006442.2, polyadenylate binding protein-interacting protein 1; HAX1; DKFZP434K046; IMAGE3455200; HYOU1; IDN3; JUNB; KRT8; KIAA0100; KIAA0102; APH-1A; LSM4; MAGED2; MRPS7; MOCS2; MNDA; NDUFA8; NNT; NFIL3; PWP1; NR4A2; NUDT4; ORMDL2; PDAP2; PPIH; PBX3; P4HA2; PPP1R15A; PRGII P2RX4; SUi1; SUi1; SUi1; RAB5C; ARHB; RNASE4; RNH; RNPC4; SEC23B; SERPINA1; SH3GLB1; SLC35B1; SOX9; SOX9; STCH; SDHC; TINF2; TCF8; E2-EPF; FOS; JUN; ZFP36; ZNF500; and ZDHHC4 as compared to a control group indicated that the individual is affected with lung cancer.
The probeset numbers as referred to herein and throughout the specification, refer to the Affymetrix probesets.
The methods to identify the airway transcriptomes can be used to identify airway transcriptomes in other animals than humans by performing the statistical comparisons as provided in the Examples below in any two animal groups, wherein one group is exposed to an airway pollutant and the other group is not exposed to such pollutant and performing the gene expression analysis of any large probeset, such as the probeset of 7119 genes used in some of the Examples. Therefore, the subject or individual as described herein and throughout the specification is not limited to human, but encompasses other mammals and animals, such as murine, bovine, swine, and other primates. This methodology can also be carried out with lung disorders to create new clusters of genes wherein change in their expression is related to specific disorders.
We identified a subset of three current smokers who did not upregulate expression of a number of predominantly redox/xenobiotic genes to the same degree as other smokers. One of these smokers developed lung cancer within 6 months of the analysis. In addition, there is a never smoker, who is an outlier among never smokers and expresses a subset of genes at the level of current smokers (see
These divergent patterns of gene expression in a small subset of smokers represent a failure of these smokers to mount an appropriate response to cigarette exposure and indicate a linkage to increased risk for developing lung cancer. As a result, these “outlier” genes can thus serve as biomarkers for susceptibility to the carcinogenic effects of cigarette smoke and other air pollutants.
Therefore, in one embodiment, the invention provides a method of determining an increased risk of lung disease, such as lung cancer, in a smoker comprising taking an airway sample from the individual, analyzing the expression of at least one, preferably at least two, still more preferably at least 4, still more preferably at least 5, still more preferably at least 6, still more preferably at least 7, still more preferably at least 8, still more preferably at least 8, and still more preferably at least all 9 of the outlier genes including AKRICI; MSNIB; TM4SF1; UPKIB; FLJ20152; SEC14L3; HT021; GALNT6; and AKRIC2, wherein deviation of the expression of at least one, preferably at least two, still more preferably at least 4, still more preferably at least 5, still more preferably at least 6, still more preferably at least 7, still more preferably at least 8, still more preferably at least 8, and still more preferably at least all 9 as compared to a control group is indicative of the smoker being at increased risk of developing a lung disease, for example, lung cancer.
We have shown that if the cells in the airways of an individual exposed to pollutant, such as cigarette smoke, do not turn on, or increase the expression of one or more of the certain genes encoding proteins associated with detoxification, and genes encoding mucins and cell adhesion molecules, this individual is at increased risk of developing lung diseases.
We have also shown that if the cells in the airways of an individual exposed to pollutant, such as cigarette smoke, do not turn off, or decrease the transcription of genes encoding one or more of certain proteins associated with immune regulation and metallothioneins, the individual has an increased risk of developing lung disease.
We have also shown that if the cells in the airways of an individual exposed to pollutant, such as cigarette smoke, do not turn off one or more tumor suppressor genes or turn on one or more protooncogenes, the individual is at increased risk of developing lung disease.
The methods disclosed herein can also be used to show exposure of a non-smoker to environmental pollutants by showing increased expression in a biological sample taken from the airways of the non-smoker of genes encoding proteins associated with detoxification, and genes encoding mucins and cell adhesion molecules or decreased expression of genes encoding certain proteins associated with immune regulation and metallothioneins. If such changes are observed, an entire group of individuals at work or home environment of the exposed individual may be analyzed and if any of them does not show the indicative increases and decreases in the expression of the airway transcriptome, they may be at greater risk of developing a lung disease and susceptible for intervention. These methods can be used, for example, in a work place screening analyses, wherein the results are useful in assessing working environments, wherein the individuals may be exposed to cigarette smoke, mining fumes, drilling fumes, asbestos and/or other chemical and/or physical airway pollutants. Screening can be used to single out high risk workers from the risky environment to transfer to a less risky environment.
Accordingly, in one embodiment, the invention provides prognostic and diagnostic methods to screen for individuals at risk of developing diseases of the lung, such as lung cancer, comprising screening for changes in the gene expression pattern of the airway transcriptome. The method comprises obtaining a cell sample from the airways of an individual and measuring the level of expression of 1-85 gene transcripts of the airway transcriptome as provided herein. Preferably, the level of at least two, still more preferably at least 3, 4, 5, 6, 7, 8, 9, 10 transcripts, and still more preferably, the level of at least 10-15, 15-20, 20-50, or more transcripts, and still more preferably all of the 97 trasncripts in the airway transcriptome are measured, wherein difference in the expression of at least one, preferably at least two, still more preferably at least three, and still more preferably at least 4, 5, 6, 7, 8, 9, 10, 10-15, 15-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-85 genes present in the airway transcriptome compared to a normal airway transcriptome is indicative of increased risk of a lung disease. The control being at least one, preferably a group of more than one individual exposed to the same pollutant and having a normal or healthy response to the exposure.
In one embodiment, difference in at least one of the detoxification related genes, mucin genes, and/or cell adhesion related genes compared to the level of these genes expressed in a control, is indicative of the individual being at an increased risk of developing diseases of the lung. The differences in expression of at least one immune system regulation and/or metallothionein regulation related genes compared to the level of these genes expressed in a control group indicates that the individual is at risk of developing diseases of the lung.
In one embodiment, the invention provides a prognostic method for lung diseases comprising detecting gene expression changes in at least on of the mucin genes of the airway transcriptome, wherein increase in the expression compared with control group is indicative of an increased risk of developing a lung disease. Examples of mucin genes include muc 5 subtypes A, B, and. C.
In one preferred embodiment, the invention provides a tool for screening for changes in the airway transcriptome during long time intervals, such as weeks, months, or even years. The airway transcriptome expression analysis is therefore performed at time intervals, preferably two or more time intervals, such as in connection with an annual physical examination, so that the changes in the airway transcriptome expression pattern can be tracked in individual basis. The screening methods of the invention are useful in following up the response of the airways to a variety of pollutants that the subject is exposed to during extended periods. Such pollutants include direct or indirect exposure to cigarette smoke or other air pollutants. The control as used herein is a healthy individual, whose responses to airway pollutants are in the normal range of a smoker as provided by, for example, the transcription patterns shown in
Analysis of transcript levels according to the present invention can be made using total or messenger RNA or proteins encoded by the genes identified in the airway trascriptome of the present invention as a starting material. In the preferred embodiment the analysis is an immunohistochemical analysis with an antibody directed against at least one, preferably at least two, still more preferably at least 4-10 proteins encoded by the genes of the airway transcriptome
The methods of analyzing transcript levels of one or more of the 85 transcripts in an individual include Northern-blot hybridization, ribonuclease protection assay, and reverse transcriptase polymerase chain reaction (RT-PCR) based methods. The different RT-PCR based techniques are the most suitable quantification method for diagnostic purposes of the present invention, because they are very sensitive and thus require only a small sample size which is desirable for a diagnostic test. A number of quantitative RT-PCR based methods have been described and are useful in measuring the amount of transcripts according to the present invention. These methods include RNA quantification using PCR and complementary DNA (cDNA) arrays (Shalon et al., Genome Research 6(7):639-45, 1996; Bernard et al., Nucleic Acids Research 24(8):1435-42, 1996), solid-phase mini-sequencing technique, which is based upon a primer extension reaction (U.S. Pat. No. 6,013,431, Suomalainen et al. Mol. Biotechnol. June; 15(2):123-31, 2000), ion-pair high-performance liquid chromatography (Doris et al. J. Chromatogr. A May 8; 806(1):47-60, 1998), and 5′ nuclease assay or real-time RT-PCR (Holland et al. Proc Natl Acad Sci USA 88: 7276 7280, 1991).
Methods using RT-PCR and internal standards differing by length or restriction endonuclease site from the desired target sequence allowing comparison of the standard with the target using gel electrophoretic separation methods followed by densitometric quantification of the target have also been developed and can be used to detect the amount of the transcripts according to the present invention (see, e.g., U.S. Pat. Nos. 5,876,978; 5,643,765; and 5,639,606).
For example, the present invention provides a method for detecting risk of developing lung cancer in a subject exposed to cigarette smoke comprising measuring the level of 1-97 proteins encoded by the airway transcriptome in a biological sample of the subject. Preferably at least one, still more preferably at least two, still more preferably at least three, and still more preferably at least 4-10, or more of the proteins encoded by the airway transcriptome in a biological sample of the subject are analyzed. The method comprises binding an antibody against one or more of the proteins encoded by the airway transcriptome (the “protein”) to a solid support chosen from the group consisting of dip-stick and membrane; incubating the solid support in the presence of the sample to be analyzed under conditions where antibody-antigen complexes form; incubating the support with an anti-protein antibody conjugated to a detectable moeity which produces a signal; visually detecting said signal, wherein said signal is proportional to the amount of protein in said sample; and comparing the signal in said sample to a standard, wherein a difference in the amount of the protein in the sample compared to said standard of at feast one, preferably at least two, still more preferably at least 3-5, still more preferably at least 5-10, proteins is indicative of an increased risk of developing lung cancer. The standard levels are measured to indicate expression levels in a normal airway exposed to cigarette smoke, as exemplified in the smoker transcript pattern shown, for example on
The assay reagents, pipettes/dropper, and test tubes may be provided in the form of a kit. Accordingly, the invention further provides a test kit for visual detection of one or more proteins encoded by the airway transcriptome, wherein detection of a level that differs from a pattern in a control individual is considered indicative of an increased risk of developing lung disease in the subject. The test kit comprises one or more solutions containing a known concentration of one or more proteins encoded by the airway transcriptome (the “protein”) to serve as a standard; a solution of a anti-protein antibody bound to an enzyme; a chrotnogen which changes color or shade by the action of the enzyme; a solid support chosen from the group consisting of dip-stick and membrane carrying on the surface thereof an antibody to the protein.
The inventions disclosed herein contemplate either one dipstick capable of detecting all the diagnostically important gene products or alternatively, a series of dipsticks capable of detecting the amount proteins of a smaller sub-group of diagnostic proteins of the present invention.
Antibodies can be prepared by means well known in the art. The term “antibodies” is meant to include monoclonal antibodies, polyclonal antibodies and antibodies prepared by recombinant nucleic acid techniques that are selectively reactive with a desired antigen. Antibodies against the proteins encoded by any of the genes in the diagnostic gene groups of the present invention are either known or can be easily produced using the methods well known in the art. Internet sites such as Biocompare through the World Wide Web at “biocompare.com/abmatrix.asp?antibody=y” provide a useful tool to anyone skilled in the art to locate existing antibodies against any of the proteins provided according to the present invention.
Antibodies against the diagnostic proteins according to the present invention can be used in standard techniques such as Western blotting or immunohistochemistry to quantify the level of expression of the proteins of the diagnostic airway proteome. This is quantified according to the expression of the gene transcript, i.e. the increased expression of transcript corresponds to increased expression of the gene product, i.e. protein. Similarly decreased expression of the transcript corresponds to decreased expression of the gene product or protein. Detailed guidance of the increase or decrease of expression of preferred transcripts in lung disease, particularly lung cancer, is set forth in the tables. For example, Tables 5 and 6 describe a group of genes the expression of which is altered in lung cancer.
Immunohistochemical applications include assays, wherein increased presence of the protein can be assessed, for example, from a saliva or sputum sample.
The immunohistochemical assays according to the present invention can be performed using methods utilizing solid supports. The solid support can be a any phase used in performing immunoassays, including dipsticks, membranes, absorptive pads, beads, microtiter wells, test tubes, and the like. Preferred are test devices which may be conveniently used by the testing personnel or the patient for self-testing, having minimal or no previous training Such preferred test devices include dipsticks, membrane assay systems as described in U.S. Pat. No. 4,632,901. The preparation and use of such conventional test systems is well described in the patent, medical, and scientific literature. If a stick is used, the anti-protein antibody is bound to one end of the stick such that the end with the antibody can be dipped into the solutions as described below for the detection of the protein. Alternatively, the samples can be applied onto the antibody-coated dipstick or membrane by pipette or dropper or the like.
The antibody against proteins encoded by the diagnostic airway transcriptome (the “protein”) can be of any isotype, such as IgA, IgG or IgM, Fab fragments, or the like. The antibody may be a monoclonal or polyclonal and produced by methods as generally described, for example, in Harlow and Lane, Antibodies, A Laboratory Manual, Cold Spring Harbor Laboratory, 1988, incorporated herein by reference. The antibody can be applied to the solid support by direct or indirect means. Indirect bonding allows maximum exposure of the protein binding sites to the assay solutions since the sites are not themselves used for binding to the support. Preferably, polyclonal antibodies are used since polyclonal antibodies can recognize different epitopes of the protein thereby enhancing the sensitivity of the assay.
The solid support is preferably non-specifically blocked after binding the protein antibodies to the solid support. Non-specific blocking of surrounding areas can be with whole or derivatized bovine serum albumin, or albumin from other animals, whole animal serum, casein, non-fat milk, and the like.
The sample is applied onto the solid support with bound protein-specific antibody such that the protein will be bound to the solid support through said antibodies. Excess and unbound components of the sample are removed and the solid support is preferably washed so the antibody-antigen complexes are retained on the solid support. The solid support may be washed with a washing solution which may contain a detergent such as Tween-20, Tween-80 or sodium dodecyl sulfate.
After the protein has been allowed to bind to the solid support, a second antibody which reacts with protein is applied. The second antibody may be labeled, preferably with a visible label. The labels may be soluble or particulate and may include dyed immunoglobulin binding substances, simple dyes or dye polymers, dyed latex beads, dye-containing liposomes, dyed cells or organisms, or metallic, organic, inorganic, or dye solids. The labels may be bound to the protein antibodies by a variety of means that are well known in the art. In some embodiments of the present invention, the labels may be enzymes that can be coupled to a signal producing system. Examples of visible labels include alkaline phosphatase, beta-galactosidase, horseradish peroxidase, and biotin. Many enzyme-chromogen or enzyme-substrate-chromogen combinations are known and used for enzyme-linked assays. Dye labels also encompass radioactive labels and fluorescent dyes.
Simultaneously with the sample, corresponding steps may be carried out with a known amount or amounts of the protein and such a step can be the standard for the assay. A sample from a healthy individual exposed to a similar air pollutant such as cigarette smoke, can be used to create a standard for any and all of the diagnostic gene group encoded proteins.
The solid support is washed again to remove unbound labeled antibody and the labeled antibody is visualized and quantified. The accumulation of label will generally be assessed visually. This visual detection may allow for detection of different colors, for example, red color, yellow color, brown color, or green color, depending on label used. Accumulated label may also be detected by optical detection devices such as reflectance analyzers, video image analyzers and the like. The visible intensity of accumulated label could correlate with the concentration of protein in the sample. The correlation between the visible intensity of accumulated label and the amount of the protein may be made by comparison of the visible intensity to a set of reference standards. Preferably, the standards have been assayed in the same way as the unknown sample, and more preferably alongside the sample, either on the same or on a different solid support.
The concentration of standards to be used can range from about 1 mg of protein per liter of solution, up to about 50 mg of protein per liter of solution. Preferably, two or more different concentrations of an airway gene group encoded proteins are used so that quantification of the unknown by comparison of intensity of color is more accurate.
For example, the present invention provides a method for detecting risk of developing lung cancer in a subject exposed to cigarette smoke comprising measuring the transcription profile of the proteins encoded by one or more groups of genes of the invention in a biological sample of the subject. Preferably at least about 30, still more preferably at least about 36, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, or about 180 of the proteins encoded by the airway transcriptome in a biological sample of the subject are analyzed. The method comprises binding an antibody against each protein encoded by the gene in the gene group (the “protein”) to a solid support chosen from the group consisting of dip-stick and membrane; incubating the solid support in the presence of the sample to be analyzed under conditions where antibody-antigen complexes form; incubating the support with an anti-protein antibody conjugated to a detectable moiety which produces a signal; visually detecting said signal, wherein said signal is proportional to the amount of protein in said sample; and comparing the signal in said sample to a standard, wherein a difference in the amount of the protein in the sample compared to said standard of the same group of proteins, is indicative of diagnosis of or an increased risk of developing lung cancer. The standard levels are measured to indicate expression levels in an airway exposed to cigarette smoke where no cancer has been detected.
The assay reagents, pipettes/dropper, and test tubes may be provided in the form of a kit. Accordingly, the invention further provides a test kit for visual detection of the proteins encoded by the airway gene groups, wherein detection of a level that differs from a pattern in a control individual is considered indicative of an increased risk of developing lung disease in the subject. The test kit comprises one or more solutions containing a known concentration of one or more proteins encoded by the airway transcriptome (the “protein”) to serve as a standard; a solution of a anti-protein antibody bound to an enzyme; a chromogen which changes color or shade by the action of the enzyme; a solid support chosen from the group consisting of dip-stick and membrane carrying on the surface thereof an antibody to the protein. Instructions including the up or down regulation of the each of the genes in the groups as provided by the Tables 1 and 2 are included with the kit.
The practice of the present invention may employ, unless otherwise indicated, conventional techniques and descriptions of organic chemistry, polymer technology, molecular biology (including recombinant techniques), cell biology, biochemistry, and immunology, which are within the skill of the art. Such conventional techniques include polymer array synthesis, hybridization, ligation, and detection of hybridization using a label. Specific illustrations of suitable techniques can be had by reference to the example herein below. However, other equivalent conventional procedures can, of course, also be used. Such conventional techniques and descriptions can be found in standard laboratory manuals such as Genome Analysis: A Laboratory Manual Series (Vols. I-IV), Using Antibodies: A Laboratory Manual, Cells: A Laboratory Manual, PCR Primer: A Laboratory Manual, and Molecular Cloning: A Laboratory Manual (all from Cold Spring Harbor Laboratory Press), Stryer, L. (1995) Biochemistry (4th Ed.) Freeman, N.Y., Gait, “Oligonucleotide Synthesis: A Practical Approach” 1984, IRL Press, London, Nelson and Cox (2000), Lehninger, Principles of Biochemistry 3rd Ed., W.H. Freeman Pub., New York, N.Y. and Berg et al. (2002) Biochemistry, 5th Ed., W.H. Freeman Pub., New York, N.Y., all of which are herein incorporated in their entirety by reference for all purposes.
The methods of the present invention can employ solid substrates, including arrays in some preferred embodiments. Methods and techniques applicable to polymer (including protein) array synthesis have been described in U.S. Ser. No. 09/536,841, WO 00/58516, U.S. Pat. Nos. 5,143,854, 5,242,974, 5,252,743, 5,324,633, 5,384,261, 5,405,783, 5,424,186, 5,451,683, 5,482,867, 5,491,074, 5,527,681, 5,550,215, 5,571,639, 5,578,832, 5,593,839, 5,599,695, 5,624,711, 5,631,734, 5,795,716, 5,831,070, 5,837,832, 5,856,101, 5,858,659, 5,936,324, 5,968,740, 5,974,164, 5,981,185, 5,981,956, 6,025,601, 6,033,860, 6,040,193, 6,090,555, 6,136,269, 6,269,846 and 6,428,752, in PCT Applications Nos. PCT/US99/00730 (International Publication Number WO 99/36760) and PCT/US01/04285, which are all incorporated herein by reference in their entirety for all purposes.
Patents that describe synthesis techniques in specific embodiments include U.S. Pat. Nos. 5,412,087, 6,147,205, 6,262,216, 6,310,189, 5,889,165, and 5,959,098. Nucleic acid arrays are described in many of the above patents, but the same techniques are applied to polypeptide and protein arrays.
Nucleic acid arrays that are useful in the present invention include, but are not limited to those that are commercially available from Affymetrix (Santa Clara, Calif.) under the brand name GeneChip7. Example arrays are shown on the website at affymetrix.com.
Examples of gene expression monitoring, and profiling methods that are useful in the methods of the present invention are shown in U.S. Pat. Nos. 5,800,992, 6,013,449, 6,020,135, 6,033,860, 6,040,138, 6,177,248 and 6,309,822. Other examples of uses are embodied in U.S. Pat. Nos. 5,871,928, 5,902,723, 6,045,996, 5,541,061, and 6,197,506.
The present invention also contemplates sample preparation methods in certain preferred embodiments. Prior to or concurrent with expression analysis, the nucleic acid sample may be amplified by a variety of mechanisms, some of which may employ PCR. See, e.g., PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992); PCR Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic Press, San Diego, Calif., 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); and U.S. Pat. Nos. 4,683,202, 4,683,195, 4,800,159 4,965,188, and 5,333,675, and each of which is incorporated herein by reference in their entireties for all purposes. The sample may be amplified on the array. See, for example, U.S. Pat. No. 6,300,070 and U.S. patent application Ser. No. 09/513,300, which are incorporated herein by reference.
Other suitable amplification methods include the ligase chain reaction (LCR) (e.g., Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988) and Barringer et al. Gene 89:117 (1990)), transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA 86, 1173 (1989) and WO88/10315), self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA, 87, 1874 (1990) and WO90/06995), selective amplification of target polynucleotide sequences (U.S. Pat. No. 6,410,276), consensus sequence primed polymerase chain reaction (CP-PCR) (U.S. Pat. No. 4,437,975), arbitrarily primed polymerase chain reaction (AP-PCR) (U.S. Pat. No. 5,413,909, 5,861,245) and nucleic acid based sequence amplification (NABSA). (U.S. Pat. Nos. 5,409,818, 5,554,517, and 6,063,603). Other amplification methods that may be used are described in, U.S. Pat. Nos. 5,242,794, 5,494,810, 4,988,617 and in U.S. Ser. No. 09/854,317, each of which is incorporated herein by reference.
Additional methods of sample preparation and techniques for reducing the complexity of a nucleic sample are described, for example, in Dong et al., Genome Research 11, 1418 (2001), in U.S. Pat. No. 6,361,947, 6,391,592 and U.S. Patent application Ser. Nos. 09/916,135, 09/920,491, 09/910,292, and 10/013,598.
Methods for conducting polynucleotide hybridization assays have been well developed in the art. Hybridization assay procedures and conditions will vary depending on the application and are selected in accordance with the general binding methods known including those referred to in: Maniatis et al. Molecular Cloning: A Laboratory Manual (2nd Ed. Cold Spring Harbor, N.Y. 1989); Berger and Kimmel Methods in Enzymology, Vol. 152, Guide to Molecular Cloning Techniques (Academic Press, Inc., San Diego, Calif., 1987); Young and Davism, P.N.A.S, 80: 1194 (1983). Methods and apparatus for carrying out repeated and controlled hybridization reactions have been described, for example, in U.S. Pat. Nos. 5,871,928, 5,874,219, 6,045,996 and 6,386,749, 6,391,623 each of which are incorporated herein by reference
The present invention also contemplates signal detection of hybridization between the sample and the probe in certain embodiments. See, for example, U.S. Pat. Nos. 5,143,854, 5,578,832; 5,631,734; 5,834,758; 5,936,324; 5,981,956; 6,025,601; 6,141,096; 6,185,030; 6,201,639; 6,218,803; and 6,225,625, in provisional U.S. Patent application 60/364,731 and in PCT Application PCT/US99/06097 (published as WO99/47964).
Examples of methods and apparatus for signal detection and processing of intensity data are disclosed in, for example, U.S. Pat. Nos. 5,143,854, 5,547,839, 5,578,832, 5,631,734, 5,800,992, 5,834,758; 5,856,092, 5,902,723, 5,936,324, 5,981,956, 6,025,601, 6,090,555, 6,141,096, 6,185,030, 6,201,639; 6,218,803; and 6,225,625, in U.S. Patent application 60/364,731 and in PCT Application PCT/US99/06097 (published as WO99/47964).
The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, e.g. Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nd ed., 2001).
The present invention also makes use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, for example, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170.
Additionally, the present invention may have embodiments that include methods for providing gene expression profile information over networks such as the Internet as shown in, for example, U.S. patent applications Ser. Nos. 10/063,559, 60/349,546, 60/376,003, 60/394,574, 60/403,381.
Throughout this specification, various aspects of this invention are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 10-20 should be considered to have specifically disclosed sub-ranges such as from 10-13, from 10-14, from 10-15, from 11-14, from 11-16, etc., as well as individual numbers within that range, for example, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20. This applies regardless of the breadth of the range. In addition, the fractional ranges are also included in the exemplified amounts that are described. Therefore, for example, a range of 1-3 includes fractions such as 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, etc. This applies particularly to the amount of increase or decrease of expression of any particular gene or transcript.
Methods and systems of the present disclosure may be combined with or modified by other methods or systems, such as, for example, those described in WO/2005/000098, which is entirely incorporated herein by reference.
The present invention has many preferred embodiments and relies on many patents, applications and other references for details known to those of the art. Therefore, when a patent, application, or other reference is cited or repeated throughout the specification, it should be understood that it is incorporated by reference in its entirety for all purposes as well as for the proposition that is recited.
In this study, we used three study groups: 1) normal non-smokers (n=23); 2) smokers without cancer (active v. former smokers) (n=52); 3) smokers with suspect cancer (n=98: 45 cancer, 53 no cancer).
We obtained epithelial nucleic acids (RNA/DNA) from epithelial cells in mouth and airway (bronchoscopy). We also obtained nucleic acids from blood to provide one control.
We analyzed gene expression using RNA and U133A Affymetrix array that represents transcripts from about 22,500 genes.
The microarray data analysis was performed as follows. We first scanned the Affymetrix chips that had been hybridized with the study group samples. The obtained microarray raw data consisted of signal strength and detection p-value. We normalized or scaled the data, and filtered the poor quality chips based on images, control probes, and histograms according to standard Affymetrix instructions. We also filtered contaminated specimens which contained non-epithelial cells. Lastly, the genes of importance were filtered using detection p-value. This resulted in identification of transcripts present in normal airways (normal airway transcriptome), with variability and multiple regression analysis. This also resulted in identification of effects of smoking on airway epithelial cell transcription. For this, we used T-test and Pearson correlation analysis. We also identified a group or a set of transcripts that were differentially expressed in samples with lung cancer and samples without cancer. This analysis was performed using class prediction models.
We used weighted voting method. The weighted voting method ranks, and gives a weight “p” to all genes by the signal to noise ration of gene expression between two classes: P=mean(class 1)−mean(class 2)/sd(class 1)=sd(class 2). Committees of variable sizes of the top ranked genes were used to evaluate test samples, but genes with more significant p-values were more heavily weighed. Each committee genes in test sample votes for one class or the other, based on how close that gene expression level is to the class 1 mean or the class 2 mean. V(gene A)=P(gene A), i.e. level of expression in test sample less the average of the mean expression values in the two classes. Votes for each class were tallied and the winning class was determined along with prediction strength as PS=Vwin−Vlose/Vwin+Vlose. Finally, the accuracy was validated using cross-validation +/− independent samples.
The results of the weighted voting method for a 50 gene group analysis (50 gene committee) were as follows. Cross-validation (n=74) resulted in accuracy of 81%, with sensitivity of 76% and specificity of 85%. In an independent dataset (n=24) the accuracy was 88%, with sensitivity of 75% and specificity of 100%.
We note that with sensitivity to bronchoscopy alone only 18/45 (40%) of cancers were diagnosed at the time of bronchoscopy using brushings, washings, biopsy or Wang.
We performed a gene expression analysis of the human genome using isolated nucleic acid samples comprising lung cell transcripts from individuals. The chip used was the Human Genome U133 Set. We used Microarray Suite 5.0 software to analyze raw data from the chip (i.e. to convert the image file into numerical data). Both the chip and the software are proprietary materials from Affymetrix. Bronchoscopy was performed to obtain nucleic acid samples from 98 smoker individuals.
We performed a Student's t-test using gene expression analysis of 45 smokers with lung cancer and 53 smokers without lung cancer. We identified several groups of genes that showed significant variation in their expression between smokers with cancer and smokers without cancer. We further identified at least three groups of genes that, when their expression was analyzed in combination, the results allowed us to significantly increase diagnostic power in identifying cancer carrying smokers from smokers without cancer.
The predictor groups of genes were identified using the GenePattern server from the Broad Institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used. GenePattern is available at World Wide Web from broad.mitedu/cancer/software/genepattern. This program allows analysis of data in groups rather than as individual genes.
Table 1 shows the top 96 genes from our analysis with different expression patterns in smokers with cancer and smokers without cancer.
Table 2 shows the 84 genes that were also identified in our previous screens as individual predictors of lung cancer.
Table 4 shows a novel group of 36 genes the expression of which was different between the smokers with cancer and smokers without cancer.
Table 3 shows a group of 50 genes that we identified as most predictive of development of cancer in smokers. That is, that when the expression of these genes was analyzed and reflected the pattern (expression down or up) as shown in Table 3, we could identify the individuals who will develop cancer based on this combined expression profile of these genes. When used in combination, the expression analysis of these 50 genes was predictive of a smoker developing lung cancer in over 70% of the samples. Accuracy of diagnosis of lung cancer in our sample was 80-85% on cross-validation and independent dataset (accuracy includes both the sensitivity and specificity). The sensitivity (percent of cancer cases correctly diagnosed) was approximately 75% as compared to sensitivity of 40% using standard bronchoscopy technique. (Specificity is percent of non-cancer cases correctly diagnosed).
These data show the dramatic increase of diagnostic power that can be reached using the expression profiling of the gene groups as identified in the present study.
We report here a gene expression profile, derived from histologically normal large airway epithelial cells of current and former smokers with clinical suspicion of lung cancer that is highly sensitive and specific for the diagnosis of lung cancer. This airway signature is effective in diagnosing lung cancer at an early and potentially resectable stage. When combined with results from bronchoscopy (i.e. washings, brushings, and biopsies of the affected area), the expression profile is diagnostic of lung cancer in 95% of cases. We further show that the airway epithelial field of injury involves a number of genes that are differentially expressed in lung cancer tissue, providing potential information about pathways that may be involved in the genesis of lung cancer.
Patient Population: We obtained airway brushings from current and former smokers (n=208) undergoing fiber optic bronchoscopy as a diagnostic study for clinical suspicion of lung cancer between January 2003 and May 2005. Patients were recruited from 4 medical centers: Boston University Medical Center, Boston, Mass.; Boston Veterans Administration, West Roxbury, Mass.; Lahey Clinic, Burlington, Mass.; and Trinity College, Dublin, Ireland. Exclusion criteria included never smokers, cigar smokers and patients on a mechanical ventilator at the time of their bronchoscopy. Each subject was followed clinically, post-bronchoscopy, until a final diagnosis of lung cancer or an alternate benign diagnosis was made. Subjects were classified as having lung cancer if their bronchoscopy studies (brushing, bronchoalveolar lavage or endobronchial biopsy) or a subsequent lung biopsy (transthoracic biopsy or surgical lung biopsy) yielded tumor cells on pathology/cytology. Subjects were classified with an alternative benign diagnosis if the bronchoscopy or subsequent lung biopsy yielded a non-lung cancer diagnosis or if their radiographic abnormality resolved on follow up chest imaging. The study was approved by the Institutional Review Boards of all 4 medical centers and all participants provided written informed consent.
Airway epithelial cell collection: Following completion of the standard diagnostic bronchoscopy studies, bronchial airway epithelial cells were obtained from the “uninvolved” right mainstem bronchus with an endoscopic cytobrush (Cellebrity Endoscopic Cytobrush, Boston Scientific, Boston, Mass.). If a suspicious lesion (endobronchial or submucosal) was seen in the right mainstem bronchus, cells were then obtained from the uninvolved left mainstem bronchus. The brushes were immediately placed in TRIzol reagent (Invitrogen, Carlsbad, Calif.) after removal from the bronchoscope and kept at −80° C. until RNA isolation was performed. RNA was extracted from the brushes using TRIzol Reagent (Invitrogen) as per the manufacturer protocol, with a yield of 8-15 μg of RNA per patient. Integrity of the RNA was confirmed by denaturing gel electrophoresis. Epithelial cell content and morphology of representative bronchial brushing samples was quantified by cytocentrifugation (ThermoShandon Cytospin, Pittsburgh, Pa.) of the cell pellet and staining with a cytokeratin antibody (Signet, Dedham Mass.). These samples were reviewed by a pathologist who was blinded to the diagnosis of the patient.
Microarray data acquisition and preprocessing: 6-8 μg of total RNA was processed, labeled, and hybridized to Affymetrix HG-U133A GeneChips containing approximately 22,215 human transcripts as described previously(17). We obtained sufficient quantity of high quality RNA for microarray studies from 152 of the 208 samples. The quantity of RNA obtained improved during the course of the study so that 90% of brushings yielded sufficient high quality RNA during the latter half of the study. Log-normalized probe-level data was obtained from CEL files using the Robust Multichip Average (RMA) algorithm(18). A z-score filter was employed to filter out arrays of poor quality (see supplement for details), leaving 129 samples with a final diagnosis available for analysis.
Microarray Data Analysis: Class Prediction
To develop and test a gene expression predictor capable of distinguishing smokers with and without lung cancer, 60% of samples (n=77) representing a spectrum of clinical risk for lung cancer and approximately equal numbers of cancer and no cancer subjects were randomly assigned to a training set (see Supplement). Using the training set samples, the 22,215 probesets were filtered via ANCOVA using pack-years as the covariate; probesets with a p-value greater than 0.05 for the difference between the two groups were excluded. This training-set gene filter was employed to control for the potential confounding effect of cumulative tobacco exposure, which differed between subjects with and without cancer (see Table 1a).
Table 1a shows demographic features and characteristics of the two patient classes being studied. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate.
Gene selection was conducted through internal cross-validation within the training set using the weighted voting algorithm(19). The internal cross-validation was repeated 50 times, and the top 40 up-and top 40 down-regulated probesets in cancer most frequently chosen during internal cross-validation runs were selected as the final gene committee of 80 features (see sections, infra, for details regarding the algorithm and the number of genes selected for the committee).
The accuracy, sensitivity, and specificity of the biomarker were assessed on the independent test set of 52 samples. This was accomplished by using the weighted vote algorithm to predict the class of each test set sample based on the gene expression of the 80 probesets and the probe set weights derived from the 77 samples in the training set. To assess the performance of our classifier, we first created 1000 predictors from the training set where we randomized the training set class labels. We evaluated the performance of these “class-randomized” classifiers for predicting the sample class of the test set samples and compared these to our classifier using ROC analysis. To assess whether the performance of our gene expression profile depends on the specific training and test sets from which it was derived and tested, we next created 500 new training and test sets with our 129 samples and derived new “sample-randomized” classifiers from each of these training sets which were then tested on the corresponding test set. To assess the specificity of our classifier genes, we next created 500 classifiers each composed of 80 randomly selected genes. We then tested the ability of these “gene-randomized” classifiers to predict the class of samples in the test set. To evaluate the robustness of our class prediction algorithm and data preprocessing, we also used these specific 80 genes to generate predictive models with an alternate class prediction algorithm (Prediction Analysis of Microarrays (PAM)(20)) and with MAS 5.0 generated expression data instead of RMA. Finally, the performance of our predictor was compared to the diagnostic yield of bronchoscopy.
Quantitative PCR Validation: Real time PCR (QRT-PCR) was used to confirm the differential expression of a select number of genes in our predictor. Primer sequences were designed with Primer Express software (Applied Biosystems, Foster City, Calif.). Forty cycles of amplification, data acquisition, and data analysis were carried out in an ABI Prism 7700 Sequence Detector (Applied Biosystems, Foster City, Calif.). All real time PCR experiments were carried out in triplicate on each sample (see sections infra).
Linking to lung cancer tissue microarray data: The 80-gene lung cancer biomarker derived from airway epithelium gene expression was evaluated for its ability to distinguish between normal and cancerous lung tissue using an Affymetrix HGU95Av2 dataset published by Bhattacharjee et al(21) that we processed using RMA. By mapping Unigene identifiers, 64 HGU95Av2 probesets were identified that measure the expression of genes that corresponded to the 80 probesets in our airway classifier. This resulted in a partial airway epithelium signature that was then used to classify tumor and normal samples from the dataset. In addition, PCA analysis of the lung tissue samples was performed using the expression of these 64 probesets.
To further assess the statistical significance of the relationship between datasets, Gene Set Enrichment Analysis(22) was performed to determine if the 64 biomarker genes are non-randomly distributed within the HGU95Av2 probesets ordered by differential expression between normal and tumor tissue. Finally, a two-tailed Fisher Exact Test was used to test if the proportion of biomarker genes among the genes differentially expressed between normal and tumor lung tissue is different from the overall proportion of differentially expressed genes (see sections, infra).
Statistical Analysis: RMA was performed in BioConductor. The upstream gene filtering by ANCOVA, and the implementation of the weighted voted algorithm and internal cross validation used to generate the data were executed through an R script we wrote for this purpose. The PAM algorithm was carried out using the ‘pamr’ library in R. All other statistical analyses including Student's T-Tests, Fisher's exact tests, ROC curves and PCA were performed using the R statistical package.
Study Population and Epithelial samples: 129 subjects that had microarrays passing the quality control filter described above were included in the class prediction analysis (see Supplemental
Class Prediction analysis: Comparison of demographic features for 77 subjects in the training set vs. the 52 samples in the test set is shown in Supplemental Table 2. An 80 gene class prediction committee capable of distinguishing smokers with and without cancer was built on the training set of 77 samples and tested on the independent sample set (
The 80-gene predictor's accuracy, sensitivity and specificity on the 52 sample test set was significantly better than the performance of classifiers derived from randomizing the class labels of the training set (p=0.004; empiric p-value for random classifier AUC>true classifier AUC;
Real time PCR: Differential expression of select genes in our diagnostic airway profile was confirmed by real time PCR (see
Linking to lung cancer tissue: Our airway biomarker was also able to correctly classify lung cancer tissue from normal lung tissue with 98% accuracy. Principal Component Analysis demonstrated separation of non-cancerous samples from cancerous samples in the Bhattacharjee dataset according to the expression of our airway signature (see
Synergy with Bronchoscopy: Bronchoscopy was diagnostic (via endoscopic brushing, washings or biopsy of the affected region) in 32/60 (53%) of lung cancer patients and 5/69 non-cancer patients. Among non-diagnostic bronchoscopies (n=92), our class prediction model had an accuracy of 85% with 89% sensitivity and 83% specificity. Combining bronchoscopy with our gene expression signature resulted in a 95% diagnostic sensitivity (57/60) across all cancer subjects. Given the approximate 50% disease prevalence in our cohort, a negative bronchoscopy and negative gene expression signature for lung cancer resulted in a 95% negative predictive value (NPV) for disease (
Stage and cell type subgroup analysis: The diagnostic yield of our airway gene expression signature vs. bronchoscopy according to stage and cell type of the lung cancer samples is shown in
Lung cancer is the leading cause of death from cancer in the United States, in part because of the lack of sensitive and specific diagnostic tools that are useful in early-stage disease. With approximately 90 million former and current smokers in the U.S., physicians increasingly encounter smokers with clinical suspicion for lung cancer on the basis of an abnormal radiographic imaging study and/or respiratory symptoms. Flexible bronchoscopy represents a relatively noninvasive initial diagnostic test to employ in this setting. This study was undertaken in order to develop a gene expression-based diagnostic, that when combined with flexible bronchoscopy, would provide a sensitive and specific one-step procedure for the diagnosis of lung cancer. Based on the concept that cigarette smoking creates a respiratory tract “field defect”, we examined the possibility that profiles of gene expression in relatively easily accessible large airway epithelial cells would serve as an indicator of the amount and type of cellular injury induced by smoking and might provide a diagnostic tool in smokers who were being evaluated for the possibility of lung cancer.
We have previously shown that smoking induces a number of metabolizing and anti-oxidant genes, induces expression of several putative oncogenes and suppresses expression of several potential tumor suppressor genes in large airway epithelial cells(17). We show here that the pattern of airway gene expression in smokers with lung cancer differs from smokers without lung cancer, and the expression profile of these genes in histologically normal bronchial epithelial cells can be used as a sensitive and specific predictor of the presence of lung cancer. We found that the expression signature was particularly useful in early stage disease where bronchoscopy was most often negative and where most problems with diagnosis occur. Furthermore, combining the airway gene expression signature with bronchoscopy results in a highly sensitive diagnostic approach capable of identifying 95% of lung cancer cases.
Given the unique challenges to developing biomarkers for disease using DNA microarrays(23), we employed a rigorous computational approach in the evaluation of our dataset. The gene expression biomarker reported in this paper was derived from a training set of samples obtained from smokers with suspicion of lung cancer and was tested on an independent set of samples obtained from four tertiary medical centers in the US and Ireland. The robust nature of this approach was confirmed by randomly assigning samples into separate training and test sets and demonstrating a similar overall accuracy (FIG. 11). In addition, the performance of our biomarker was significantly better than biomarkers obtained via randomization of class labels in the training set (
In terms of limitations, our study was not designed to assess performance as a function of disease stage or subtype. Our gene expression predictor, however, does appear robust in early stage disease compared with bronchoscopy (see
In addition to serving as a diagnostic biomarker, profiling airway gene expression across smokers with and without lung cancer can also provide insight into the nature of the “field of injury” reported in smokers and potential pathways implicated in lung carcinogenesis. Previous studies have demonstrated allelic loss and methylation of tumor suppressor genes in histologically normal bronchial epithelial cells from smokers with and without lung cancer(12; 13; 15). Whether these changes are random mutational effects or are directly related to lung cancer has been unclear. The finding that our airway gene signature was capable of distinguishing lung cancer tissue from normal lung (
Among the 80 genes in our diagnostic signature, a number of genes associated with the RAS oncogene pathway, including Rab 1a and FOS, are up regulated in the airway of smokers with lung cancer. Rab proteins represent a family of at least 60 different Ras-like GTPases that have crucial roles in vesicle trafficking, signal transduction, and receptor recycling, and dysregulation of RAB gene expression has been implicated in tumorigenesis(24). A recent study by Shimada et al.(25) found a high prevalence of Rab1A-overexpression in head and neck squamous cell carcinomas and also in premalignant tongue lesions, suggesting that it may be an early marker of smoking-related respiratory tract carcinogenesis.
In addition to these RAS pathway genes, the classifier contained several pro-inflammatory genes, including Interleukin-8 (IL-8) and beta-defensin 1 that were up regulated in smokers with lung cancer. IL-8, originally discovered as a chemotactic factor for leukocytes, has been shown to contribute to human cancer progression through its mitogenic and angiogenic properties(26; 27). Beta defensins, antimicrobial agents expressed in lung epithelial cells, have recently found to be elevated in the serum of patients with lung cancer as compared to healthy smokers or patients with pneumonia(28). Higher levels of these mediators of chronic inflammation in response to tobacco exposure may result in increased oxidative stress and contribute to tumor promotion and progression in the lung(29; 30)
A number of key antioxidant defense genes were found to be decreased in airway epithelial cells of subjects with lung cancer, including BACH2 and dual oxidase 1, along with a DNA repair enzyme, DNA repair protein 1C. BACH-2, a transcription factor, promotes cell apoptosis in response to high levels of oxidative-stress(31). We have previously found that a subset of healthy smokers respond differently to tobacco smoke, failing to induce a set of detoxification enzymes in their normal airway epithelium, and that these individuals may be predisposed to its carcinogenic effects(17). Taken together, these data suggest that a component of the airway “field defect” may reflect whether a given smoker is appropriately increasing expression of protective genes in response to the toxin. This inappropriate response may reflect a genetic susceptibility to lung cancer or alternatively, epigenetic silencing or deletion of that gene by the carcinogen.
In summary, our study has identified an airway gene expression biomarker that has the potential to directly impact the diagnostic evaluation of smokers with suspect lung cancer. These patients usually undergo fiberoptic bronchoscopy as their initial diagnostic test. Gene expression profiling can be performed on normal-appearing airway epithelial cells obtained in a simple, non-invasive fashion at the time of the bronchoscopy, prolonging the procedure by only 3-5 minutes, without adding significant risks. Our data strongly suggests that combining results from bronchoscopy with the gene expression biomarker substantially improves the diagnostic sensitivity for lung cancer (from 53% to 95%). In a setting of 50% disease prevalence, a negative bronchoscopy and negative gene expression signature for lung cancer results in a 95% negative predictive value (NPV), allowing these patients to be followed non-aggressively with repeat imaging studies. For patients with a negative bronchoscopy and positive gene expression signature, the positive predictive value is ˜70%, and these patients would likely require further invasive testing (i.e. transthoracic needle biopsy or open lung biopsy) to confirm the presumptive lung cancer diagnosis. However, this represents a substantial reduction in the numbers of patients requiring further invasive diagnostic testing compared to using bronchoscopy alone. In our study, 92/129 patients were bronchoscopy negative and would have required further diagnostic work up. However, the negative predictive gene expression profile in 56 of these 92 negative bronchoscopy subjects would leave only 36 subjects who would require further evaluation (see
The cross-sectional design of our study limits interpretation of the false positive rate for our signature. Given that the field of injury may represent whether a smoker is appropriately responding to the toxin, derangements in gene expression could precede the development of lung cancer or indicate a predisposition to the disease. Long-term follow-up of the false positive cases is needed (via longitudinal study) to assess whether they represent smokers who are at higher risk for developing lung cancer in the future. If this proves to be true, our signature could serve as a screening tool for lung cancer among healthy smokers and have the potential to identify candidates for chemoprophylaxis trials.
Study Patients and Sample Collection
A. Primary sample set: We recruited current and former smokers undergoing flexible bronchoscopy for clinical suspicion of lung cancer at four tertiary medical centers. All subjects were older than 21 years of age and had no contraindications to flexible bronchoscopy including hemodynamic instability, severe obstructive airway disease, unstable cardiac or pulmonary disease (i.e. unstable angina, congestive heart failure, respiratory failure) inability to protect airway or altered level of consciousness and inability to provide informed consent. Never smokers and subjects who only smoked cigars were excluded from the study. For each consented subject, we collected data regarding their age, gender, race, and a detailed smoking history including age started, age quit, and cumulative tobacco exposure. Former smokers were defined as patients who had not smoked a cigarette for at least one month prior to entering our study. All subjects were followed, post-bronchoscopy, until a final diagnosis of lung cancer or an alternative diagnosis was made (mean follow-up time=52 days). For those patients diagnosed with lung cancer, the stage and cell type of their tumor was recorded. The clinical data collected from each subject in this study can be accessed in a relational database at http://pulm.bumc.bu.edu/CancerDx/. The stage and cell type of the 60 cancer samples used to train and test the class prediction model is shown in Supplemental Table 1 below.
Supplemental Table 1 above shows cell type and staging information for 60 lung cancer patients in the 129 primary sample set used to build and test the class prediction model. Staging information limited to the 48 non-small cell samples.
The demographic features of the samples in training and test shown are shown in Supplemental Table 2 below. The Table shows patient demographics for the primary dataset (n=129) according to training and test set status. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate. PPD=packs per day, F=former smokers, C=current smokers, M=male, F=female.
While our study recruited patients whose indication for bronchoscopy included a suspicion for lung cancer, each patient's clinical pre-test probability for disease varied. In order to ensure that our class prediction model was trained on samples representing a spectrum of lung cancer risk, three independent pulmonary clinicians, blinded to the final diagnoses, evaluated each patient's clinical history (including age, smoking status, cumulative tobacco exposure, co-morbidities, symptoms/signs and radiographic findings) and assigned a pre-bronchoscopy probability for lung cancer. Each patient was classified into one of three risk groups: low (<10% probability of lung cancer), medium (10-50% probability of lung cancer) and high (>50% probability of lung cancer). The final risk assignment for each patient was decided by the majority opinion.
Prospective Sample Set:
After completion of the primary study, a second set of samples was collected from smokers undergoing flexible bronchoscopy for clinical suspicion of lung cancer at 5 medical centers (St. Elizabeth's Hospital in Boston, Mass. was added to the 4 institutions used for the primary dataset). Inclusion and exclusion criteria were identical to the primary sample set. Forty additional subjects were included in this second validation set. Thirty-five subjects had microarrays that passed our quality-control filter. Demographic data on these subjects, including 18 smokers with primary lung cancer and 17 smokers without lung cancer, is presented in Supplemental Table 3. There was no statistical difference in age or cumulative tobacco exposure between case and controls in this prospective cohort (as opposed to the primary dataset; see Table 1a).
Supplemental Table 3 below shows patient demographics for the prospective validation set (n=35) by cancer status. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate. PPD=packs per day, F=former smokers, C=current smokers, M=male, F=female.
Airway Epithelial Cell Collection:
Bronchial airway epithelial cells were obtained from the subjects described above via flexible bronchoscopy. Following local anesthesia with 2% topical lidocaine to the oropharynx, flexible bronchoscopy was performed via the mouth or nose. Following completion of the standard diagnostic bronchoscopy studies (i.e. bronchoalveolar lavage, brushing and endo/transbronchial biopsy of the affected region), brushings were obtained via three endoscopic cytobrushes from the right mainstem bronchus. The cytobrush was rubbed over the surface of the airway several times and then retracted from the bronchoscope so that epithelial cells could be placed immediately in TRIzol solution and kept at −80° C. until RNA isolation was performed.
Given that these patients were undergoing bronchoscopy for clinical indications, the risks from our study were minimal, with less than a 5% risk of a small amount of bleeding from these additional brushings. The clinical bronchoscopy was prolonged by approximately 3-4 minutes in order to obtain the research samples. All participating subjects were recruited by IRB-approved protocols for informed consent, and participation in the study did not affect subsequent treatment. Patient samples were given identification numbers in order to protect patient privacy.
Microarray Data Acquisition and Preprocessing
Microarray data acquisition: 6-8 μg of total RNA from bronchial epithelial cells were converted into double-stranded cDNA with SuperScript II reverse transcriptase (Invitrogen) using an oligo-dT primer containing a T7 RNA polymerase promoter (Genset, Boulder, Colo.). The ENZO Bioarray RNA transcript labeling kit (Enzo Life Sciences, Inc, Farmingdale, N.Y.) was used for in vitro transcription of the purified double stranded cDNA. The biotin-labeled cRNA was then purified using the RNeasy kit (Qiagen) and fragmented into fragments of approximately 200 base pairs by alkaline treatment. Each cRNA sample was then hybridized overnight onto the Affymetrix HG-U133A array followed by a washing and staining protocol. Confocal laser scanning (Agilent) was then performed to detect the streptavidin-labeled fluor.
Preprocessing of array data via RMA: The Robust Multichip Average (RMA) algorithm was used for background adjustment, normalization, and probe-level summarization of the microarray samples in this study (Irizarry R A, et al., Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003; 31(4):e15.). RMA expression measures were computed using the R statistical package and the justRMA function in the Affymetrix Bioconductor package. A total of 296 CEL files from airway epithelial samples included in this study as well as those previously processed in our lab were analyzed using RMA. RMA was chosen for probe-level analysis instead of Microarray Suite 5.0 because it maximized the correlation coefficients observed between 7 pairs of technical replicates (Supplemental Table 4).
Supplemental Table 4 shows the Average Pearson Correlations between 7 pairs of replicate samples where probe-set gene expression values were determined using Microarray Suite 5.0 (Affy), logged data from Microarray Suite 5.0 (log 2 Affy), and RMA. RMA maximizes the correlation between replicate samples.
Sample filter: To filter out arrays of poor quality, each probeset on the array was z-score normalized to have a mean of zero and a standard deviation of 1 across all 152 samples. These normalized gene-expression values were averaged across all probe-sets for each sample. The assumption explicit in this analysis is that poor-quality samples will have probeset intensities that consistently trend higher or lower across all samples and thus have an average z-score that differs from zero. This average z-score metric correlates with Affymetrix MAS 5.0 quality metrics such as percent present (
Prospective validation test set: CEL files for the additional 40 samples were added to the collection of airway epithelial CEL files described above, and the entire set was analyzed using RMA to derive expression values for the new samples. Microarrays that had an average z-score with a value greater than 0.129 (5 of the 40 samples) were filtered out. Class prediction of the 35 remaining prospective samples was conducted using the vote weights for the 80-predictive probesets derived from the training set of 77 samples using expression values computed in the section above.
Microarray Data Analysis
Class Prediction Algorithm: The 129-sample set (60 cancer samples, 69 no cancer samples) was used to develop a class-prediction algorithm capable of distinguishing between the two classes. One potentially confounding difference between the two groups is a difference in cumulative tobacco-smoke exposure as measured by pack-years. To insure that the genes chosen for their ability to distinguish patients with and without cancer in the training set were not simply distinguishing this difference in tobacco smoke exposure, the pack-years each patient smoked was included as a covariate in the training set ANCOVA gene filter.
In addition, there are differences in the pre-bronchoscopy clinical risk for lung cancer among the 129 patients. Three physicians reviewed each patient's clinical data (including demographics, smoking histories, and radiographic findings) and divided the patients into three groups: high, medium, and low pre-bronchoscopy risk for lung cancer (as described above). In order to control for differences in pre-bronchoscopy risk for lung cancer between the patients with and without a final diagnosis of lung cancer, the training set was constructed with roughly equal numbers of cancer and no cancer samples from a spectrum of lung cancer risk.
The weighted voting algorithm (Golub T R, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286(5439):531-537) was implemented as the class prediction method, with several modifications to the gene-selection methodology. Genes that varied between smokers with and without cancer in the training set samples after adjusting for tobacco-smoke exposure (p<0.05) were identified using an ANCOVA with pack-years as the covariate. Further gene selection was performed using the signal to noise metric and internal cross-validation where the 40 most consistently up- and the 40 most consistently down-regulated probesets were identified. The internal cross validation involved leaving 30% of the training samples out of each round of cross-validation, and selecting genes based on the remaining 70% of the samples. The final gene committee consisted of eighty probesets that were identified as being most frequently up-regulated or down-regulated across 50 rounds of internal cross-validation. The parameters of this gene-selection algorithm were chosen to maximize the average accuracy, sensitivity and specificity obtained from fifty runs. This algorithm was implemented in R and yields results that are comparable to the original implementation of the weighted-voted algorithm in GenePattern when a specific training, test, and gene set are given as input.
After determination of the optimal gene-selection parameters, the algorithm was run using a training set of 77 samples to arrive at a final set of genes capable of distinguishing between smokers with and without lung cancer. The accuracy, sensitivity and specificity of this classifier were tested against 52 samples that were not included in the training set. The performance of this classifier in predicting the class of each test-set sample was assessed by comparing it to runs of the algorithm where either: 1) different training/tests sets were used; 2) the cancer status of the training set of 77 samples were randomized; or 3) the genes in the classifier were randomly chosen (see randomization section below for details).
Randomization: The accuracy, sensitivity, specificity, and area under the ROC curve (using the signed prediction strength as a continuous cancer predictor) for the 80-probeset predictor (above) were compared to 1000 runs of the algorithm using three different types of randomization. First, the class labels of the training set of 77 samples were permuted and the algorithm, including gene selection, was re-run 1000 times (referred to in Supplemental Table 5 as Random 1).
Supplemental Table 5 below shows results of a comparison between the actual classifier and random runs (explained above). Accur=Accuracy, Sens=Sensitivity, Spec=Specificity, AUC=area under the curve, and sd=standard deviation. All p-value are empirically derived.
The second randomization used the 80 genes in the original predictor but permuted the class labels of the training set samples over 1000 runs to randomize the gene weights used in the classification step of the algorithm (referred to in Supplemental Table 5 as Random 2).
In both of these randomization methods, the class labels were permuted such that half of the training set samples was labeled correctly. The third randomization method involved randomly selecting 80 probesets for each of 1000 random classifiers (referred to in Supplemental Table 5 as Random 3).
The p-value for each metric and randomization method shown indicate the percentage of 1000 runs using that randomization method that exceeded or was equal to the performance of the actual classifier.
In addition to the above analyses, the actual classifier was compared to 1000 runs of the algorithm where different training/test sets were chosen but the correct sample labels were retained. Empirically derived p-values were also computed to compare the actual classifier to the 1000 runs of the algorithm (see Supplemental Table 6). These data indicate that the actual classifier was derived using a representative training and test set.
Supplemental Table 6 above shows a comparison of actual classifier to 1000 runs of the algorithm with different training/test sets.
Finally, these 1000 runs of the algorithm were also compared to 1000 runs were the class labels of different training sets were randomized in the same way as described above. Empirically derived p-values were computed to compare 1000 runs to 1000 random runs (Supplemental Table 7).
Supplemental Table 7 above shows comparison of runs of the algorithm using different training/test sets to runs where the class labels of the training sets were randomized (1000 runs were conducted).
The distribution of the prediction accuracies summarized in Supplemental Tables 6 and 7 is shown in
Characteristics of the 1000 additional runs of the algorithm: The number of times a sample in the test set was classified correctly and its average prediction strength was computed across the 1000 runs of the algorithm. The average prediction strength when a sample was classified correctly was 0.54 for cancers and 0.61 for no cancers, and the average prediction strength when a sample was misclassified was 0.31 for cancer and 0.37 for no cancers. The slightly higher prediction strength for smokers without cancer is reflective of the fact that predictors have a slightly higher specificity on average. Supplemental
In order to further assess the stability of the biomarker gene committee, the number of times the 80-predictive probesets used in the biomarker were selected in each of the 1000 runs (Supplemental Table 6) was examined. (See
Comparison of RMA vs. MAS 5.0 and weighted voting vs. PAM: To evaluate the robustness of our ability to use airway gene expression to classify smokers with and without lung cancer, we examined the effect of different class-prediction and data preprocessing algorithms. We tested the 80-probesets in our classifier to generate predictive models using the Prediction Analysis of Microarrays (PAM) algorithm (Tibshirani R, et al., Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002; 99(10):6567-6572), and we also tested the ability of the WV algorithm to use probeset level data that had been derived using the MAS 5.0 algorithm instead of RMA. The accuracy of the classifier was similar when microarray data was preprocessed in MAS 5.0 and when the PAM class prediction algorithm was used (see Supplemental Table 8).
Supplemental Table 8 shows a comparison of accuracy, sensitivity and specificity for our 80 probeset classifier on the 52 sample test set using alternative microarray data preprocessing algorithms and class prediction algorithms.
Prediction strength: The Weighted voting algorithm predicts a sample's class by summing the votes each gene on the class prediction committee gives to one class versus the other. The level of confidence with which a prediction is made is captured by the Prediction Strength (PS) and is calculated as follows:
Vwinning refers to the total gene committee votes for the winning class and Vlosing refers to the total gene committee votes for the losing class. Since Vwinning is always greater than Vlosing, PS confidence varies from 0 (arbitrary) to 1 (complete confidence) for any given sample.
In our test set, the average PS for our gene profile's correct predictions (43/52 test samples) is 0.73(+/−0.27), while the average PS for the incorrect predictions (9/52 test samples) is much lower: 0.49(+/−0.33; p<z; Student T-Test). This result shows that, on average, the Weighted Voting algorithm is more confident when it is making a correct prediction than when it is making an incorrect prediction. This result holds across 1000 different training/test set pairs (
Cancer cell type: To determine if the tumor cell subtype affects the expression of genes that distinguish airway epithelium from smokers with and without lung cancer, Principal Component Analysis (PCA) was performed on the gene-expression measurements for the 80 probesets in our predictor and all of the airway epithelium samples from patients with lung cancer (
Link to Lung Cancer Tissue Microarray Dataset
Preprocessing of Bhattacharjee data: The 254 CEL files from HgU95Av2 arrays used by Bhattacharjee et al. (Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001; 98(24):13790-13795) were downloaded from the MIT Broad Institute's database available through internet (broad.mit.edu/mpr/lung). RMA-derived expression measurements were computed using these CEL files as described above. Technical replicates were filtered by choosing one at random to represent each patient. In addition, arrays from carcinoid samples and patients who were indicated to have never smoked were excluded, leaving 151 samples. The z-score quality filter described above was applied to this data set resulting in 128 samples for further analysis (88 adenocarcinomas, 3 small cell, 20 squamous, and 17 normal lung samples).
Probesets were mapped between the HGU133A array and HGU95Av2 array using Chip Comparer at the Duke University's database available through the world wide web at tenero.duhs.duke.edu/genearray/perl/chip/chipcomparer.pl. 64 probesets on the HGU95Av2 array mapped to the 80-predictive probesets. The 64 probesets on the HGU95Av2 correspond to 48 out of the 80 predictive probesets (32/80 predictive probesets have no clear corresponding probe on the HGU95Av2 array).
Analyses of Bhattacharjee dataset: In order to explore the expression of genes that we identified as distinguishing large airway epithelial cells from smokers with and without lung cancer in lung tumors profiled by Bhattacharjee, two different analyses were performed. Principal component analysis was used to organize the 128 Bhattacharjee samples according to the expression of the 64 mapped probesets. Principal component analysis was conducted in R using the package prcomp on the z-score normalized 128 samples by 64 probeset matrix. The normal and malignant samples in the Bhattacharjee dataset appear to separate along principal component 1 (see
The second analysis involved using the weighted voted algorithm to predict the class of 108 samples in the Bhattacharjee dataset using the 64 probe sets and a training set of 10 randomly chosen normal tissues and 10 randomly chosen tumor tissues. The samples were classified with 89.8% accuracy, 89.1% sensitivity, and 100% specificity (see Supplemental Table 9 below, Single Run). To examine the significance of these results, the weighted voted algorithm was re-run using two types of data randomization. First, the class labels of the training set of 20 samples were permuted and the algorithm, including gene selection, was re-run 1000 times (referred to in Supplemental Table 9 as Random 1). The second randomization involved permuting the class labels of the training set of 20 samples and re-running the algorithm 1000 times keeping the list of 64-probsets constant (referred to in Supplemental Table 9 as Random 2). In the above two types of randomization, the class labels were permuted such that half the samples were correctly labeled. The p-value for each metric and randomization method shown indicate the percentage of 1000 runs using that randomization method that exceeded or were equal to the performance of the actual classifier. Genes that distinguish between large airway epithelial cells from smokers with and without cancer are significantly better able to distinguish lung cancer tissue from normal lung tissue than any random run where the class labels of the training set are randomized.
Supplemental Table 9 above shows results of a comparison between the predictions of the Bhattacharjee samples using the 64 probesets that map to a subset of the 80-predictive probesets and random runs (explained above). Accur=Accuracy, Sens=Sensitivity, Spec=Specificity, AUC=area under the curve, and sd=standard deviation.
Real Time PCR: Quantitative RT-PCR analysis was used to confirm the differential expression of a seven genes from our classifier. Primer sequences for the candidate genes and a housekeeping gene, the 18S ribosomal subunit, were designed with PRIMER EXPRESS® software (Applied Biosystems) (see Supplemental Table 10).
Primer sequences for five other housekeeping genes (HPRT1, SDHA, YWHAZ, GAPDH, and TBP) were adopted from Vandesompele et al. (Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002; 3(7)). RNA samples (1 μg of the RNA used in the microarray experiment) were treated with DNAfree (Ambion, Austin, Tex.), according to the manufacturer's protocol, to remove contaminating genomic DNA. Total RNA was reverse-transcribed using random hexamers (Applied Biosystems) and SuperScript II reverse transcriptase (Invitrogen). The resulting first-strand cDNA was diluted with nuclease-free water (Ambion) to 5 ng/μl. PCR amplification mixtures (25 μl) contained 10 ng template cDNA, 12.5 μl of 2× SYBR Green PCR master mix (Applied Biosystems) and 300 nM forward and reverse primers. Forty cycles of amplification and data acquisition were carried out in an Applied Biosystems 7500 Real Time PCR System. Threshold determinations were automatically performed by Sequence Detection Software (version 1.2.3) (Applied Biosystems) for each reaction. All real-time PCR experiments were carried out in triplicate on each sample (6 samples total; 3 smokers with lung cancer and 3 smokers without lung cancer).
Data analysis was performed using the geNorm tool (Id.). Three genes (YWHAZ, GAPDH, and TBP) were determined to be the most stable housekeeping genes and were used to normalize all samples. Data from the QRT-PCR for 7 genes along with the microarray results for these genes is shown in
(1) Parkin D M, et al., C A Cancer J Clin 2005; 55(2):74-108.
(2) Shields P G. Ann Oncol 1999; 10 Suppl 5:S7-11.
(3) Hirsch F R, et al., Clin Cancer Res 2001; 7(1):5-22.
(4) Jett J R. Clin Cancer Res 2005; 11(13 Pt 2):49885-4992s.
(5) Macredmond R, et al., Thorax 2006; 61(1):54-56.
(6) Postmus P E. Chest 2005; 128(1):16-18.
(7) Mazzone P, et al., Clin Chest Med 2002; 23(1):137-58, ix.
(8) Schreiber G, and McCrory DC. Chest 2003; 123(1 Suppl):115S-128S.
(9) Janssen-Heijnen M L, et al., Epidemiology 2001; 12(2):256-258.
(10) Salomaa E R, et al., Chest 2005; 128(4):2282-2288.
(11) Auerbach O, et al., Arch Environ Health 1970; 21(6):754-768.
(12) Powell C A, et al., Clin Cancer Res 1999; 5(8):2025-2034.
(13) Wistuba I I, et al., J Natl Cancer Inst 1997; 89(18):1366-1373.
(14) Franklin W A, et al., J Clin Invest 1997; 100(8):2133-2137.
(15) Guo M, et al., Clin Cancer Res 2004; 10(15):5131-5136.
(16) Miyazu Y M, et al., Cancer Res 2005; 65(21):9623-9627.
(17) Spira A, et al., Proc Natl Acad Sci USA 2004; 101(27):10143-10148.
(18) Bolstad B M, et al., Bioinformatics 2003; 19(2):185-193.
(19) Golub T R, et al, Science 1999; 286(5439):531-537.
(20) Tibshirani R, et al., Proc Natl Acad Sci USA 2002; 99(10):6567-6572.
(21) Bhattacharjee A, et al., Proc Natl Acad Sci USA 2001; 98(24):13790-13795.
(22) Subramanian A, et al., Proc Natl Acad Sci USA 2005; 102(43):15545-15550.
(23) Simon R, et al., J Natl Cancer Inst 2003; 95(1):14-18.
(24) Cheng K W, et al., Cancer Res 2005; 65(7):2516-2519.
(25) Shimada K, et al., Br J Cancer 2005; 92(10):1915-1921.
(26) Xie K. Cytokine Growth Factor Rev 2001; 12(4):375-391.
(27) Campa D, et al., Cancer Epidemiol Biomarkers Prey 2005; 14(10):2457-2458.
(28) Arimura Y, et al., Anticancer Res. 24, 4051-4057. 2004.
(29) Coussens L M, and Werb Z. Nature 2002; 420(6917):860-867.
(30) Godschalk R, et al., Carcinogenesis 2002; 23(12):2081-2086.
(31) Kamio T, et al., Blood 2003; 102(9):3317-3322
Primary lung tumors and histologically normal lung tissue were collected from the tumor bank of Brigham and Women's Hospital. Research specimens were snap frozen on dry ice and stored at −140° C. Each sample was accompanied by an adjacent section embedded in Optimum Cutting Temperature Compound for histological confirmation The thoracic surgery clinical data-base was abstracted for details of smoking history, clinical staging and other demographic details. From the tumor bank, six cases of adenocarcinoma in life-time never smokers were selected and six cases of adenocarcinoma from cigarette smokers were then chosen for comparison by matching for the following criteria in a descending hierarchy of priority: (1) cell type; (2) histological stage of differentiation; (3) pathologic TNM stage; and (4) patient age (Table 6). All of the subjects except for one smoker were female. The collection of anonymous discarded tumor specimens was approved by the Brigham and Womens Institutional Review Board Hospital and the study was approved by the Human Studies Committee of Boston University Medical Center. Once the cases were selected, specimens and clinical data were de-identified in accordance with the discarded tissue protocol governing the study; thus, linkage of each paired tumor and normal tissue sample with specific additional clinical characteristics other than smoking status, cell type, differentiation and gender was not possible.
Histological sections were reviewed by a pathologist, blinded to original pathological diagnosis. Tumor histology agreed in all cases and the mean percentage of tumor in each sample was 60%, DNA was extracted from tumor and non-involved samples using QIAamp Tissue Kit (Qiagen, Valencia, Calif.). LOH studies were performed using fluorescent microsatellite LOH analysis as described previously (Powell Calif., et al., Clin. Cancer Res., 5:2025-34 (1999)).
Tumor and normal lung DNA templates from samples were amplified with a panel of 52 fluorescent PCR primers from ten chromosomal regions that have been reported to harbor lung cancer tumor suppressor genes or have demonstrated LOH in lung tumors or bronchial epithelium of cigarette smokers. Based on our prior studies and results of other investigators using fluorescent methods to detect LOH, we defined LOH as a >20% change in normalized allele height ratio (
The extent of LOH was expressed as fractional allelic loss (FAL) which equals the number of primers with LOH per template/number of informative primers. Fisher exact test and ×2 were used to determine the difference in FAL in smokers compared with nonsmokers.
Results. All tumors demonstrated LOH in at least one microsatellite on each of the ten chromosomal arms evaluated in this study (Table 7). With respect to nonmalignant lung epithelium, LOH was more frequent in the tumors of nonsmokers than in those of smokers (
Chromosomes 1.0p, 9p, and 5q were the most frequent sites of LOH in nonsmokers' tumors while 9p and 5q were the most frequent sites in smokers. Increased FAL in nonsmokers was most pronounced at five chromosomal arms: 3p, 8p, 9p, 10p, and 18q with FAL ranging from 55 to 87%. These microsatellites harbor several known or candidate tumor suppressor genes such as FHIT, DLCL (Daigo Y, et al., Cancer Res., 59:1966-1972 (1999)), RASSFI (Dammam R, el al., Nat. Genet., 25:315-319 (2000)) (chromosome 3p), PRK (Li B, et al., J Biol. Chem., 271:19402-19408 (1996) (chromosome 8p), p16 (chromosome 9p), SMAD2 and SMAD4 (Takei K, etal., Cancer Res., 58:3700-3705 (1998)) (chromosome 18q).
In most tumors, there were instances of microsatellites demonstrating LOH interspersed with microsatellites that retained heterozygosity (see chromosome 1p in subject S3, Table 7). This pattern of discontinuous allelic loss was evident on all chromosomes that were evaluated, and is considered a potential mutational signature of lung carcinogenesis attributable to mitotic recombination (Wistuba, II, Behrens C, et al., Cancer Res., 60:19491960 (2000)).
However, in other instances there was LOH at a number of contiguous loci suggesting larger chromosomal deletions (see chromosome 3p in subject NS3, Table 7). This was particularly true on 3p, a fragile site previously found to be involved in smokers with and without tumors.
Methods. Samples of epithelial cells, obtained by brushing airway surfaces, were obtained from intra- and extra-pulmonary airways in 11 normal non-smokers (NS), 15 smokers without lung cancer (S), and 9 smokers with lung cancer (SC). 5-10 ug of RNA was extracted using standard trizol-based methods, quality of RNA was assayed in gels, and the RNA was processed using standard protocols developed by Affymetrix for the U133 human array. Expression profiles, predictive algorithms, and identification of critical genes are made using bioinformatic methods.
Results. There are 5169 genes in the NS Transcriptome, 4960 genes in the S Transcriptome, and 5518 genes in the SC Transcriptome. There are 4344 genes in common between the 3 Transcriptomes. There are 327 unique genes in the NS Transcriptome, 149 unique genes in the S Transcriptome, and 551 unique genes in the SC Transcriptome.
There are approximately 1.25 billion daily cigarette smokers in the world (1). Cigarette smoking is responsible for 90% of all lung cancers, the leading cause of cancer deaths in the US and the world (2, 3). Smoking is also the major cause of chronic obstructive pulmonary disease (COPD), the fourth leading cause of death in the US (4). Despite the well-established causal role of cigarette smoking in lung cancer and COPD, only 10-20% of smokers actually develop these diseases (5). There are few indicators of which smokers are at highest risk for developing either lung cancer or COPD, and it is unclear why individuals remain at high risk decades after they have stopped smoking (6).
Given the burden of lung disease created by cigarette smoking, surprisingly few studies(7, 8) have been done in humans to determine how smoking affects the epithelial cells of the pulmonary airways that are exposed to the highest concentrations of cigarette smoke or what smoking-induced changes in these cells are reversible when subjects stop smoking. With the two exceptions noted above, which examine a specific subset of genes in humans, studies investigating the effects of tobacco on airway epithelial cells have been in cultured cells, in human alveolar lavage samples in which alveolar macrophages predominate, or in rodent smoking models (summarized in Gebel et al(9)).
A number of recent studies have used DNA microarray technology to study normal and cancerous whole lung tissue and have identified molecular profiles that distinguish the various subtypes of lung cancer as well as predict clinical outcome in a subset of these patients(10-13).
Based on the concept that genetic alterations in airway epithelial cells of smokers represent a “field defect”(14, 15), we obtained human epithelial cells at bronchoscopy from brushings of the right main bronchus proximal to the right upper lobe of the lung, and defined profiles of gene expression in these cells using the U133A GeneChip® array (Affymetrix Inc., Santa Clara, Calif.). We here describe the subset of genes expressed in large airway epithelial cells (the airway transcriptome) of healthy never smokers, thereby gaining insights into the biological functions of these cells.
Surprisingly, we identified a large number of genes whose expression is altered by cigarette smoking, defined genes whose expression correlates with cumulative pack years of smoking, and identified genes whose expression does and does not return to normal when subjects discontinue smoking.
In addition, we identified a subset of smokers who were “outliers” expressing some genes in a fashion that significantly differed from most smokers. One of these “outliers” developed lung cancer within 6 months of expression profiling, suggesting that gene expression profiles of smokers with cancer differ from that of smokers without lung cancer.
Study Population and Sample Collection: We recruited non-smoking and smoking subjects (n=93) to undergo fiberoptic bronchoscopy at Boston Medical Center between November 2001 and June 2003. Non-smoking volunteers with significant environmental cigarette exposure and subjects with respiratory symptoms or regular use of inhaled medications were excluded. For each subject, a detailed smoking history was obtained including number of pack-years, number of packs per day, age started, age quit, and environmental tobacco exposure.
All subjects in our study underwent fiberoptic bronchoscopy between November 2001 and June 2003. Risks from the procedure were minimized by carefully screening volunteers (medical history, physical exam, chest X-ray, spirometry and EKG), by minimizing topical lidocaine anesthesia, and by monitoring the EKG and SaO2 throughout the procedure. After passage of the bronchoscope through the vocal cords, brushings were obtained via 3 cytobrushes (CELEBRITY Endoscopy Cytology Brush, Boston Scientific, Boston, Mass.) from the right upper lobe bronchus.
Bronchial airway epithelial cells were obtained from brushings of the right mainstem bronchus taken during fiberoptic bronchoscopy using an endoscopic cytobrush (CELEBRITY Endoscopy Cytology Brush, Boston Scientific, Boston, Mass.). The brushes were immediately placed in TRizol reagent (Invitrogen, Carlsbad, Calif.) after removal from the bronchoscope and kept at −80° C. until RNA isolation was performed. Any other RNA protection protocol known to one skilled in the art can also be used. RNA was extracted from the brushes using TRizol Reagent (Invitrogen) as per the manufacturer protocol, with a yield of 8-15 μg of RNA per patient. Other methods of RNA isolation or purification can be used to isolate RNA from the samples. Integrity of the RNA was confirmed by running it on a RNA denaturing gel. Epithelial cell content of representative bronchial brushing samples was quantified by cytocentrifugation (ThermoShandon Cytospin, Pittsburgh, Pa.) of the cell pellet and staining with a cytokeratin antibody (Signet, Dedham Mass.). The study was approved by the Institutional Review Board of Boston University Medical Center and all participants provided written informed consent.
Microarray Data Acquisition and Preprocessing: We obtained sufficient quantity of good quality RNA for microarray studies from 85 of the 93 subjects recruited into our study. Total RNA was processed, labeled, and hybridized to Affymetrix HG-U133A GeneChips containing approximately 22,500 human genes, any other type of nucleic acid or protein array may also be used. Six to eight μg of total RNA from bronchial epithelial cells was converted into double-stranded cDNA with the SuperScript II reverse transcriptase (Invitrogen) using an oligo-dT primer containing a T7 RNA polymerase promoter (Genset, Boulder, Colo.). The ENZO Bioarray RNA transcript labeling kit (Affymetrix) was used for in vitro transcription of the purified double stranded cDNA. The biotin-labeled cRNA was purified using the RNeasy kit (Qiagen) and fragmented into approximately 200 base pairs by alkaline treatment (200mM Tris-acetate, pH 8.2, 500 mM potassium acetate, 150 mM magnesium acetate). Each verified cRNA sample was then hybridized overnight onto the Affymetrix HG-U133A array and confocal laser scanning (Agilent) was then performed to detect the streptavidin-labeled fluor. A single weighted mean expression level for each gene along with a p(detection)-value (which indicates whether the transcript was reliably detected) was derived using Microarray Suite 5.0 software (Affymetrix, SantaClara, Calif.).
Using a one-sided Wilcoxon Signed rank test, the MAS 5.0 software also generated a detection p-value (p(detection)-value) for each gene which indicates whether the transcript was reliably detected. We scaled the data from each array in order to normalize the results for inter-array comparisons. Microarray data normalization was accomplished in MAS 5.0, where the mean intensity for each array (top and bottom 2% of genes excluded) was corrected (by a scaling factor) to a set target intensity of 100. The list of genes on this array is available at hap://www.affymetrix.com/analysis/download center.affx.
Arrays of poor quality were excluded based on several quality control measures. Each array's scanned image was required to be free of any significant artifacts and the bacterial genes spiked into the hybridization mix had to have a P(detection)-value below 0.05 (called present). If an array passed this criteria, it was evaluated based on three other quality measures: the 3′ to 5′ ratio of the intensity for Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the percent of genes detected as present, and the percent of “outlier” genes as determined by a computational algorithm we developed (see httn://pulm.bumc.bu.edu/aged/supplemental.html for further details, which are herein incorporated by reference).
In addition to the above set of rules, one further quality control measure was applied to each array. While cytokeratin stains of selected specimens reveal that approximately 90% of nucleated cells are epithelial, we developed a gene filter to exclude specimens potentially contaminated with inflammatory cells. A group of genes on the U133A array was identified that should be expressed in bronchial epithelial cells as well as a list of genes that are specific for various lineages of white blood cells and distal alveolar epithelial cells (see
In addition to filtering out poor quality arrays, a gene filter was applied to remove genes that were not reliably detected. From the complete set of ˜22500 probesets on the U133 array, we filtered out probesets whose p(detection)-value was not less than 0.05 in at least 20% of all samples. 9968 probesets passed our filter and were used in all further statistical analyses for the dataset.
Microarray Data Analysis: Clinical information and array data as well as gene annotations are stored in an interactive MYSQL database coded in Pert available at http://pulm.bumc.bu.edu/aged/index.html. All statistical analyses below and within the database were performed using R software version 1.6.2 (available at http://r-project.org). The gene annotations used for each probe set were from the October 2003 NetAffx HG-U13 3A Annotation Files.
Technical, spatial (right and left bronchus from same subject) and temporal (baseline and at 3 months from same subject) replicates were obtained from selected subjects for quality control. Pearson correlations were calculated for technical, spatial and temporal replicate samples from the same individual. RNA isolated from the epithelial cells of one patient was divided in half and processed separately as detailed in the methods for the technical replicates (data not shown). Different brushings were obtained from the right and left airways of the same patient and processed separately for the spatial replicates (
In addition to the correlation graphs in
The second approach uses a different methodology, but yields similar results to those described in
An unsupervised analysis of the microarray data was performed by hierarchal clustering the top 1000 most variable probe sets (determined by coefficient of variation) across all samples using log transformed z-score normalized data. The analysis was performed using a Pearson correlation (uncentered) similarity metric and average linkage clustering with CLUSTER and TREEVIEW software programs obtained at http://rana.1bI.gov/EisenSoftware.htm (see
The normal large airway transcriptome was defined by the genes whose median p(detection)-value was less than 0.05 across all 23 healthy never smokers (7119 genes expressed across majority of subjects), as well as a subset of these 7119 genes whose p(detection)-value was less than 0.05 in all 23 subjects (2382 genes expressed across all subjects). The coefficient of variation for each gene in the transcriptome was calculated. as the standard deviation divided by the mean expression level multiplied by 100 for that gene across all nonsmoking individuals. In order to identify functional categories that were over- or underrepresented within the airway transcriptome, the GOMINER software (16) was used to functionally classify the genes expressed across all nonsmokers (2382 probesets) by the molecular function categories within Gene Ontology (GO). Multiple linear regressions were performed on the top ten percent most variable probesets (712 probesets, as measured by the coefficient of variation) in the normal airway transcriptome (7119 probesets) in order to study the effects of age, gender, and race on gene expression.
It should be noted, that genes expressed at low levels are not necessarily accurately detected by microarray technology. The probe sets which define the normal airway transcriptome, therefore, will represent genes which are expressed at a measurable level in either the majority or all of the nonsmoking healthy subjects. One of the limitations to this approach, however, is that we will be excluding genes expressed at low levels in the normal airway transcriptome.
Multiple linear regressions were performed on the top ten percent most variable genes (712 genes, as measured by the coefficient of variation, defined here as sd/mean*100) in the normal airway transcriptome (7119 genes) in order to study the effects of age, gender, and race on gene expression (see
To examine the effect of smoking on the airway, a two-sample t-test was used to test for genes differentially expressed between current smokers (n=34) and never smokers (n=23). In order to quantify how well a given gene's expression level correlates with number of pack-years of smoking among current smokers, Pearson correlation coefficients were calculated (see supplementary information). For multiple comparison correction, a permutation test was used to assess the significance of our p-value threshold for any given gene's comparison between two groups (p(t-test)-value) or between a clinical variable (p(t-test)-value) (see supporting information for details). In order to further characterize the behavior of current smokers, two-dimensional hierarchical clustering of all never smokers and current smokers using the genes that were differentially expressed between current vs. never smokers was performed. Hierarchical clustering of the genes and samples was performed using log transformed z-score normalized data using a Pearson correlation (uncentered) similarity metric and average linkage clustering using CLUSTER and TREEVIEW software programs.
Multidimensional scaling and principal component analysis were used to characterize the behavior of former smokers (n=18) based on the set genes differentially expressed between current and never smokers using Partek 5.0 software (http://www.partek.com). In addition, we executed an unsupervised hierarchical clustering analysis of all 18 former smokers according to the expression of the genes differentially expressed between current and never smoker. In order to identify genes irreversibly altered by cigarette smoking, we performed at-test between former smokers (n=18) and never smokers (n=23) across the genes that were considered differentially expressed between current and never smokers. Coefficients of variation (sd/mean*100) were computed across never, former, and current smoker subjects for each of the 9968 probesets. The top 1000 most variable probesets (%CV>56.52) were selected and hierarchical clustering of these probesets and samples was performed using log transformed z-score normalized data using a Pearson correlation (uncentered) similarity metric and average linkage clustering using CLUSTER and TREEVIEW software programs obtained at http://rana.1bI.gov/EisenSoftware.htm. The clustering dendogram of the samples is displayed in
Given the invasive nature of the bronchoscopy procedure, we were unable to recruit age-, race- and gender-matched patients for the smoker vs. nonsmoker comparison. Due to baseline differences in age, gender, and race between never and current smoker groups (see
Genes that distinguish smokers with and without cancer. In order to identify airway gene expression profiles diagnostic of lung cancer, a two-sample t-test was performed to test for genes differentially expressed between smokers with lung cancer (n=23) and smokers without lung cancer (n=45). 202 genes were differentially expressed. between the groups at p<0.001 (see table 6). In order to correct for multiple comparisons, we calculated a q-value (Storey J D & Tibshirani R (2003). Proc. Natl. Acad. Sci. U.S.A 100, 9449-9445) for each gene, which represents the proportion of false positives present in the group of genes with smaller p-values than the gene.
Outlier genes among current smokers: Among airway epithelial genes altered by cigarette smoke, there are a number of genes expressed at extremely high or low levels among a subset of current smokers. In order to identify these “outlier genes,” we performed a Grubbs test on the 320 genes differentially expressed between current (n=34) and never (n=23) smokers at p<0.001. Nine genes were found to be outliers in 3 or more of the current smokers (see table 7). These divergent patterns of gene expression in a small subset of smokers represent a failure to mount an appropriate response to cigarette exposure and may be linked to increased risk for developing lung cancer. As a result, these “outlier” genes can thus serve as biomarkers for susceptibility to the carcinogenic effects of cigarette smoke.
Quantitative PCR Validation: Real time PCR(QRT-PCR) was used to confirm the differential expression of a select number of genes. Primer sequences were designed with Primer Express software (Applied Biosystems, Foster City, Calif.). Forty cycles of amplification, data acquisition, and data analysis were carried out in an ABI Prism 7700 Sequence Detector (Applied Biosystems, Foster City, Calif.). All real time PCR experiments were carried out in triplicate on each sample.
In further detail, real time PCR (QRT-PCR) primer sequences were designed with Primer Express software (Applied Biosystems, Foster City, Calif.) based on alignments of candidate gene sequences. RNA samples (500 ng of residual sample from array experiment) were treated with DNAfree (Ambion), as per the manufacturer protocol, to remove contaminating genomic DNA. Total RNA was reverse transcribed using Superscript II (Gibco). Five microliters of the reverse transcription reaction was added to 45 μl of SYBR Green PCR master mix (Applied Biosystems). Forty cycles of amplification, data acquisition, and data analysis were carried out in an ABI Prism 7700 Sequence Detector (PE Applied Biosystems). Threshold determinations were automatically performed by the instrument for each reaction. The cycle at which a sample crosses the threshold (a PCR cycle where the fluorescence emission exceeds that of nontemplate controls) is called the threshold cycle, or CT. A high CT value corresponds to a small amount of template DNA, and a low CT corresponds to a large amount of template present initially. All real time PCR experiments were carried out in triplicate on each sample (mean of the triplicate shown). Data from the QRT-PCR for 5 genes that changed in response to cigarette exposure along with the tnicroarray results for these genes is shown in
Additional Information: Additional information from this study including the raw image data from all microarray samples (.DAT files), expression levels for all genes in all samples (stored in a relational database), user-defined statistical and graphical analysis of data and clinical data on all subjects is available at http://pulm.bumc.bu.edu/aged/. Data from our microarray experiments has also been deposited in NCBI's Gene Expression Omnibus under accession GSE994.
Results and Discussion: Study Population and replicate samples. Microarrays from 75 subjects passed the quality control filters described above and are included in this study. Demographic data on these subjects, including 23 never smokers, 34 current smokers, and 18 former smokers, is presented in
The Normal Airway Transcriptome: 7119 genes were expressed at measurable levels in the majority of never smokers and 2382 genes were expressed in all of the 23 healthy never smokers. There was relatively little variation in expression levels of the 7119 genes; 90% had a coefficient of variation (SD/mean) of <50% (see
Table 6 depicts the GOMINER molecular functions(16) of the 2382 genes expressed in large airway epithelial cells of all healthy never smokers. Genes associated with oxidant stress, ion and electron transport, chaperone activity, vesicular transport, ribosomal structure and binding functions are over-represented. Genes associated with transcriptional regulation, signal transduction, pores and channels are under-represented as well as immune, cytokine and chemokine genes. Upper airway epithelial cells, at least in normal subjects, appear to serve as an oxidant and detoxifying defense system for the lung, but serve few other complex functions in the basal state.
Table 6: GOMINER molecular functions of genes in airway epithelial cells. Major molecular functional categories and subcategories of 2382 genes expressed in all never smoker subjects. Over- or under-representation of categories is deteiniined using Fisher's Exact Test. The null hypothesis is that the number of genes in our flagged set belonging to a category divided by the total number of genes in the category is equal to the number of flagged genes NOT in the category divided by the total number of genes NOT in the category. Equivalency in these two proportions is consistent with a random distribution of genes into functional categories and indicates no enrichment or depletion of genes in the category being tested. Categories considered to be statistically (p (GO)<0.05) over- or under-represented by GOMINER are shown.
Cells/arrays refers to the ratio of the number of genes expressed in epithelial cells divided by the number of genes on U133A array in each functional category. Actual numbers are in parentheses.
Effects of Cigarette Smoking on the Airway Transcriptome: Smoking altered the airway epithelial cell expression of a large number of genes. Ninety-seven genes were found to he differentially expressed by t-test between current and never smokers at p<1.06*10−5. This (p(t-test)-value) threshold was selected based on a permutation analysis performed to address the multiple comparison problem inherent in ally microarray analysis (see supporting information for further details). We chose a very stringent multiple comparison correction and (p(t-test)-value) threshold in order to identify a subset of genes altered by cigarette smoking with only a small probability of having a false positive. Of the 97 genes that passed the permutation analysis, 68 (73%) represented increased gene expression among current smokers. The greatest increases were in genes that coded for xenobiotic functions such as CYP1B1 (30 fold) and DBDD (5 fold), antioxidants such as GPX2 (3 fold), and. ALDH3A1 (6 fold) and genes involved in electron transport such as NADPH (4 fold). In addition, several cell adhesion molecules, CEACAM6 (2 fold) and claudin 10 (3 fold), were increased in smokers, perhaps in response to the increased permeability that has been found on exposure to cigarette smoke(17). Genes that decreased included TU3A (−4 fold), MMP10 (−2 fold), HLF (−2 fold), and CX3CL1 (−2 fold). In general, genes that were increased in smokers tended to be involved in regulation of oxidant stress and glutathione metabolism, xenobiotic metabolism, and secretion. Expression of several putative oncogenes (pirin, CA12, and CEACAM6) were also increased. Genes that decreased in smokers tended to be involved in regulation of inflammation, although expression of several putative tumor suppressor genes (TU3A, SLIT1 and 2, GAS6) were decreased. Changes in the expression of select genes were confirmed by real time RT-PCR (see
As might be expected, changes in gene expression were also correlated with cumulative cigarette exposure (pack-years). While 159 and 661 genes correlated with cumulative smoking history at p<0.001 and p<0.01 levels respectively (see
Due to baseline differences in age, sex, and race between never and current smoker groups, ANCOVA and 2-way ANOVA were performed to test the effect of smoking status on gene expression while controlling for the effects of age, gender, race and two-way interactions. Many of the genes found to be modulated by smoking in this analysis were also found using the simpler t-test. Age and gender had little effect on gene expression changes induced by smoking, while race appeared to influence the effect of smoking on the expression of a number of genes. The ANOVA analysis controlling for race yielded 16 genes, not included in the set of 97 genes differentially expressed between current and never smokers (see
Thus, the general effect of smoking on large airway epithelial cells was to induce expression of xenobiotic metabolism and redox stress-related genes and to decrease expression of some genes associated with regulation of inflammation. Several putative oncogenes were upregulated and tumor suppressor genes were downregulated although their roles, in smoking-induced lung cancer remain to be determined. Risk for developing lung cancer in smokers has been shown to increase with cumulative pack-years of exposure(22), and a number of putative oncogenes correlate positively with pack-years, while putative tumor suppressor genes correlate negatively.
It is unlikely that the alterations we observed in smokers were due to a change in cell types obtained at bronchoscopy. Several dynein genes were expressed at high levels in never smokers in our study, consistent with the predominance of ciliated cells in our samples. The level of expression of various dynein genes, and therefore the balance of cell types being sampled, did not change in smokers. This is consistent with a previous study of antioxidant gene expression in airway epithelial cells from never and current smokers that showed no change in histologic types of cells obtained from smokers(8). Our findings that drug metabolism and antioxidant genes are induced by smoking in airway epithelial cells is consistent with in vitro and in vivo animal studies (summarized in (9)). The high density arrays used in our studies allowed us to define the effect of cigarette smoking on a large number of genes not previously described as being affected by smoking.
Two sample unequal variance t-tests were performed to find differentially expressed genes between never and current smokers. Due to the presence of multiple comparisons in array data, there is the potential problem of finding genes differentially expressed between the 2 groups when no difference actually exists(Benjamini, Y. & Hochberg, Y. (1995) Journal of the Royal Statistical Society Series B 57, 289-300). Current methods available to adjust for multiple comparisons, such as the Bonferroni correction (where the (p(t-test)-value) threshold is divided by the number of hypotheses tested), are often too conservative when applied to microarray data (MacDonald, T. J., Brown, K. M., LaFleur, B., Peterson, K., Lawlor, C., Chen, Y., Packer, R. J., Cogen, P. & Stephan, D. A. (2001) Nat. Genet. 29, 143-152). However, we chose to employ a very stringent multiple comparison correction and (p(t-test)-value) threshold in order to identify a subset of genes altered by cigarette smoking with only a small probability of having a false positive. The Bonferroni correction controls the probability of committing even one error in all the hypotheses tested; however, the correction assumes independence of the different tests which is unlikely to hold true in the microarray setting where multiple genes are co-regulated (Tusher, V. G., Tibshirani, R. & Chu, G. (2001) Proc. Natl. Acad Sci. U.S.A 98, 5116-5121). Therefore, we have elected to employ a permutation-based correction (coded in PERL in our database) to assess the significance of the (p(t-test)-value) for any given gene. The permutation test is similar to the Bonferroni correction in that it controls the probability of finding even one gene by chance in the hypotheses tested, however, a permutation-based correction is data dependent. After calculating at-test statistic and (p(t-test)-value) for each gene, we permute the group assignments of all samples 1000 times and calculate for each permutation the t-statistic and corresponding (p(t-test)-value) for each gene. After all permutations are completed, the result is a 9968 (# of genes) by 1000 (# of permutations) matrix of (p(t-test)-values). For each permutation, a gene's actual (p(t-test)-value) is compared to all other permuted (p(t-test)-values) to determine if the any of the permuted (p(t-test)-values) is equal to or lower than the actual gene's (p(t-test)-value). An adjusted (p(t-test)-value) is computed for each gene based on the permutation test. The adjusted (p(t-test)-value) is the probability of observing at least as small a (p(t-test)-value) (in any gene) as the gene's actual (p(t-test)-value) in any random permutation. A gene is considered significant if less than 50 out of 1000 permutations (0.05) yield a gene with a permuted (p(t-test)-value) equal to or lower than the actual gene's (p(t-test)-value).
For our t-test comparing current vs. never smokers, the permuted (p(t-test)-value) threshold was found to be 1.06*10−5. Ninety-seven genes were considered differentially expressed between current and never smokers at this threshold. One shortcoming of this methodology is that is impossible to compute all possible permutations of the group assignments for large sample sizes. As a result, we repeated the permutation analysis 15 times yielding an average (p(t-test)-value) of 1.062*10−5 (sd=1.52*10−6). The mean (p(t-test)-value) was used as a cutoff and yielded a gene list of ninety-seven genes. In this case, the distribution of the data is such that the permuted P<t-test)-value threshold is slightly less strict than the equivalent Bonferroni cutoff.
By only focusing on the list of 97 genes that pass the (p(t-test)-value) threshold of 1.06*10−5, we recognize that we are ignoring a number of genes differentially expressed between never and current smokers (false negatives), but we wanted to be very confident regarding biological conclusions derived from genes that were considered differentially expressed. A broader list of genes was defined by calculating the q-value for each gene in the analysis as proposed by Storey J D & Tibshirani R (2003). Proc. Nail. Acrid Sci. US.A 100, 9449-9445. A given gene's q-value is the proportion of false positives present in the group of genes with smaller p-values than the gene. The q-value of the 97th gene was 0.005, which means that among all 97 t-tests that we designate as significant only 0.5% of them will be false positives. A less strict (p(t-test)-value) cutoff of 4.06*10−4 q-value=0.01) yields 261 genes with approximately 3 false positive genes. The q-values were calculated using the program Q-Value which can be downloaded from http://faculty.washington.edu/˜istorey/gvalue/. Larger lists of genes can be accessed through our database by selecting a less restrictive (p(t-test)-value) threshold (http://pulm.bumc.bu.edu/aged).
In order to further characterize the effect of tobacco smoke on bronchial epithelial cells, we wanted to explore how genes' expression changes with amount of smoking. Pearson correlation calculations exploring the relationship between gene expression among current smokers and pack-years of smoking were computed. A less strict permutation analysis was performed to correct for multiple Pearson correlation calculations. The analysis is analogous to the procedure described above, except only the genes having a correlation with a (p(correlation)-value) of less than 0.05 are permuted (2099 probesets instead of 9968 probesets). In addition, instead of permuting the class labels as described above, the pack-years were permuted (in a given permutation, gene expression values for a gene are assigned randomly to pack-year values). Using the less strict permutation analysis, the threshold was found to be 3.19*106 genes falling below this threshold. Supplementary Table 6 displays the top 51 genes with unadjusted (p(correlation)-value) below 0.0001. The (p(correlation)-value) threshold found using the permutation based multiple comparison correction is more strict than the Bonferroni threshold of 2.4*10−5 because the correction is data dependent and pack-year values in our study are quite variable. The current smokers in our study have an average number of pack-years of 22, but there are 3 “outlier” current smokers with extremely high pack-year histories (>70 pack-years). These smokers with extremely high pack years underpin the linear fit and result in better correlations even for random permutations, and thus lead to a stricter multiple comparison correction threshold.
Effects of Smoking Cessation: There is relatively little information about how smoking cessation alters the effects of smoking on airways. Cough and sputum production decreases rapidly in smokers with bronchitis who cease to smoke(23). The accelerated decline in forced expiratory volume (FEVI), that characterizes smokers with COPD, reverts to an ace appropriate decline of FEVI when smoking is discontinued(24). However, the allelic loss in airway epithelial cells obtained at biopsy, changes relatively little in former smokers and the risk for developing lung cancer remains high for at least 20 years after smoking cessation(6).
There were 13 genes that did not return to normal levels in former smokers, even those who had discontinued smoking 20-30 year prior to testing (p<9*10−4; threshold determined by permutation analysis). These genes include a number of potential tumor suppressor genes, e.g. TU3A and CX3CL1, that are permanently decreased, and several putative oncogenes, e.g. CEACAM6 and HN1, which are permanently increased (see
We performed an unsupervised hierarchical clustering analysis of all 18 former smokers according to the expression of the 97 genes differentially expressed between current and never smoker (
In order to identify genes irreversibly altered by cigarette smoking, we performed a t-test between former smokers (n=18) and never smokers (n=23) across the 97 genes that were considered differentially expressed between current and never smokers. A permutation analysis (as described above) was used to determine the (p(t-test)-value) threshold of 9.8*10−4 . Using this threshold, 15 of the 97 probesets were found to be significantly irreversible altered by cigarette smoking. In order to strengthen the argument that the 15 irreversibly altered probesets are related to smoking, the analysis was expanded to all 9968 genes. At-test was performed between former and never smoker across all 9968 genes, and 44 genes were found to have a (p(t-test)-value) threshold below 0.00098. While the permuted (p(t-test)-value) threshold for this extension of our t-test should have been computed across all 9968 genes, the former smokers are the smallest group in our study and thus we chose a less restrictive (p(t-test)-value) threshold. Although a there was about a 100-fold increase in the amount of genes analyzed there was only about a 3-fold increase in the number of genes found to be significantly different between never and former smokers. Therefore, most genes that are significantly different between never and former smokers are also significantly different between current and never smokers. Also, in addition to the 15 genes, 12 more genes had a (p(t-test)-value) between current and never smokers of less than 0.001, and only 7 of the 44 genes had (p(t-test)-values) between current and never smokers of greater than 0.05 (
We have, for the first time, characterized the genes expressed, and by extrapolation, defined the functions of a specific set of epithelial cells from a complex organ across a broad cross section of normal individuals. Large airway epithelial cells appear to serve antioxidant, metabolizing, and host defense functions.
Cigarette smoking, a major cause of lung disease, induces xenobiotic and redox regulating genes as well as several oncogenes, and decreases expression of several tumor suppressor genes and genes that regulate airway inflammation. We also identified a subset of three smokers who respond differently to cigarette smoke, i.e. individuals who do not turn on the genes needed to deal with getting rid of the pollutants, i.e., their airway transcriptome expression pattern resembles that of a non-smoker, and these smokers are thus predisposed to the carcinogenic effects.
Finally, we have explored the reversibility of altered gene expression when smoking was discontinued. The expression level of smoking induced genes among former smokers began to resemble that of never smokers after two years of smoking cessation. Genes that reverted to normal within two years of cessation tended to serve metabolizing and antioxidant functions.
Several genes, including potential oncogenes and tumor suppressor genes, failed to revert to never smoker levels years after cessation of smoking. Without wishing to be bound by a theory, these later findings explain the continued risk for developing lung cancer many years after individuals have ceased to smoke. In addition, results from this study show that the airway gene expression profile in smokers serves as a biomarker for lung cancer.
The present application is a continuation-in-part of U.S. application Ser. No. 15/888,831, filed on Feb. 5, 2018, which is a continuation of U.S. application Ser. No. 14/613,210, filed on Feb. 3, 2015, which is a continuation of U.S. application Ser. No. 13/524,749, filed on Jun. 15, 2012, which is a continuation of U.S. application Ser. No. 12/869,525, filed on Aug. 26, 2010, which is a continuation of U.S. application Ser. No. 11/918,588, filed Feb. 8, 2008, which is a national stage filing under 35 U.S.C. 371 of International Application PCT/US2006/014132, filed Apr. 14, 2006, which claims the benefit of priority under 35 U.S.C. 119(e) to U.S. provisional application Ser. No. 60/671,243, filed on Apr. 14, 2005, the contents of which are herein incorporated by reference in their entirety. International Application PCT/US2006/014132 was published under PCT Article 21(2) in English.
This invention was made with Government Support under Contract No. HL 071771 awarded by the National Institutes of Health. The Government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 60671243 | Apr 2005 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15888831 | Feb 2018 | US |
| Child | 16510584 | US | |
| Parent | 14613210 | Feb 2015 | US |
| Child | 15888831 | US | |
| Parent | 13524749 | Jun 2012 | US |
| Child | 14613210 | US | |
| Parent | 12869525 | Aug 2010 | US |
| Child | 13524749 | US | |
| Parent | 11918588 | Feb 2008 | US |
| Child | 12869525 | US |
