Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
  • G01N33/57407—Specifically defined cancers
  • G01N33/57423—Specifically defined cancers of lung
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/50—Determining the risk of developing a disease
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

• the presently disclosed subject matter pertains to the use of biomarkers in the detection of lung cancer in subjects with indeterminate pulmonary nodules and in the management of treatment in subjects with potential lung cancer.
• Lung cancer accounts for more cancer deaths than any other malignancy. Despite advances in diagnostic capabilities and treatment, lung cancer mortality has not significantly changed over the past several decades. Most patients present with inoperable disease when therapeutic options including chemotherapy and radiotherapy are rarely curative. Screening studies for lung cancer with chest radiographs and sputum cytology have failed to show that this approach will decrease the number of patients that die from the disease. See Marcus et al. (2000).
• CT spiral computed tomography
• FDG-PET fluorodeoxyglucose( 18 F)-positron emission tomography
• the presently disclosed subject matter provides a method for assigning a subject having an indeterminate pulmonary nodule or abnormality to a group having higher or lower probability of lung cancer, the method comprising: a) providing a subject having an indeterminate pulmonary nodule or abnormality, the indeterminate pulmonary nodule or abnormality having a size; b) determining an amount of each member of a panel of biomarkers in a sample from the subject, wherein the panel of biomarkers comprises at least two of the proteins alpha-1- antitrypsin (AAT), carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), retinol binding protein (RBP), transferrin, and haptoglobin; and c) assigning the subject to a group having a higher or lower probability of lung cancer based on the determined amount of each biomarker member in the panel and based on the size of the indeterminate pulmonary nodule or abnormality.
• AAT alpha-1- antitryp
• the panel of biomarkers comprises at least AAT, CEA, and SCC. In some embodiments, the panel of biomarkers is AAT, CEA, and SCC.
• the method comprises assigning the nodule or abnormality to a size category selected from the group comprising a small size category, an intermediate size category, and a large size category.
• the nodule or abnormality is assigned to the small size category when the largest of the length, width and height of the nodule or abnormality is less than one centimeter; assigned to the intermediate size category when the largest of the length, width and height of the nodule or abnormality is at least one centimeter and less than three centimeters; or assigned to the large size category when the largest of the length, width and height of the nodule or abnormality is three centimeters or more.
• the method comprises applying a predetermined algorithm to the determined amount of each biomarker in the panel and the size of the indeterminate pulmonary nodule or abnormality; and using the results of the algorithm to assign the subject to a group having a higher or lower probability of lung cancer.
• the algorithm is a logistic regression analysis or a classification and regression tree (CART) analysis.
• the predetermined algorithm is selected based on the size of the indeterminate pulmonary nodule or abnormality.
• a CART analysis algorithm is selected when the indeterminate pulmonary nodule or abnormality has a largest dimension that is 3 centimeters or more.
• a logistic regression analysis algorithm is selected when the indeterminate pulmonary nodule or abnormality has a largest dimension that is less than 3 centimeters.
• the logistic regression analysis algorithm is a weighted logistic regression analysis algorithm when the indeterminate pulmonary nodule or abnormality has a largest dimension that is at least one centimeter and less than 3 centimeters.
• the sample is a serum sample. In some embodiments, the subject is a human subject.
• the presently disclosed subject matter provides a method for managing treatment of a subject with potential lung cancer, the method comprising: a) providing a subject having an indeterminate pulmonary nodule or abnormality, the indeterminate pulmonary nodule or abnormality having a size; b) determining an amount of each member of a panel of biomarkers in a sample from a subject having an indeterminate pulmonary nodule or abnormality, wherein the panel of biomarkers comprises at least two of the proteins AAT, CEA, SCC, RBP, transferrin and haptoglobin; c) assigning the subject to a group having a higher or lower probability of lung cancer based on the determined amount of each biomarker member in the panel and based on the size of the indeterminate pulmonary nodule or abnormality; and d) managing the treatment of the subject with potential lung cancer based on the group to which the subject is assigned and based on the size of the indeterminate pulmonary nodule or abnormality.
• the panel of biomarkers comprises at least
• the panel of biomarkers is AAT, CEA, and SCC.
• Figure 1A is a bar graph showing the number of malignant ("Cancer", light grey bars) and benign ("No cancer", dark grey bars) pulmonary nodules in the subjects of a training set based on nodule size, i.e., small, less than 1 centimeters (cm); intermediate, 1-2.9 cm; or large, > 3 cm.
• nodule size i.e., small, less than 1 centimeters (cm); intermediate, 1-2.9 cm; or large, > 3 cm.
• Figure 1 B is a bar graph showing the number of malignant ("Cancer", light grey bars) and benign ("No cancer", dark grey bars) pulmonary nodules in the subjects of a validation set based on nodule size, i.e., small, less than 1 centimeters (cm); intermediate, 1-2.9 cm; or large, > 3 cm.
• Figure 2 is a graphical depiction of the results of Classification and Regression Tree (CART) analysis of a training set of 509 serum specimens from individuals with (Cancer) or without (No Cancer) lung cancer.
• the analysis had 9 terminal nodes and was based on pulmonary nodule size (SIZE) and serum concentration of two protein biomarkers, carcinoembryonic antigen (CEA) and alpha-1 antitrypsin (AAT).
• SIZE pulmonary nodule size
• CEA carcinoembryonic antigen
• AAT alpha-1 antitrypsin
• Lung cancer can be detected based on analyzing, in combination, (i) the levels of biomarkers in a panel of biomarkers and (ii) the size of an indeterminate pulmonary nodule or abnormality present in the subject being assessed.
• the presently provided methods can suggest which patients with indeterminate pulmonary nodules or abnormalities have lung cancer (for example, can suggest which patients have a high risk of having lung cancer).
• the phrase “consisting of” excludes any element, step, or ingredient not specified in the claim.
• amino acid sequence and terms such as “peptide”, “polypeptide” and “protein” are used interchangeably herein, and are not meant to limit the amino acid sequence to the complete, native amino acid sequence (i.e. a sequence containing only those amino acids found in the protein as it occurs in nature) associated with the recited protein molecule.
• the proteins and protein fragments of the presently disclosed subject matter can be produced by recombinant approaches or can be isolated from a naturally occurring source.
• the protein fragments can be any size, and for example can range in size from four amino acid residues to the entire amino acid sequence minus one amino acid.
• antibody includes any antibody fragments that bind with sufficient specificity to a protein of interest.
• detection molecule is used herein in its broadest sense to include any molecule that can bind with sufficient specificity to one of the members of the biomarker panel to allow for detection of the particular biomarker member in the presence or absence of the other members of the panel. To allow for detection can mean to determine the presence or absence of the particular biomarker member and, in some embodiments, can mean to determine the amount of the particular biomarker. Detection molecules can include antibodies and antibody fragments.
• sample is used in its broadest sense. In one sense, it is meant to include a specimen from a biological source.
• Biological samples can be obtained from animals (including humans) and encompass fluids (e.g., blood, mucus, urine, saliva), solids, tissues, cells, and gases.
• Biological samples include blood products, such as plasma, serum and the like.
• size when used herein with regard to an indeterminate pulmonary nodule or abnormality can refer to the measure of the largest dimension (i.e., length, width, height) of the nodule or abnormality. When the nodule or abnormality is approximately spherical, the largest dimension can also be the diameter. Thus, “size”, “diameter,” and “largest dimension” can be used interchangeably herein.
• the size can be determined from an imaging scan (e.g., a CT scan) or by physical examination if the nodule is removed or exposed during surgery. In some embodiments, "size” can refer to a size category encompassing a range of particular measured sizes.
• a specific binding partner for each of the detection molecules is used herein to include any molecule that binds with sufficient specificity to one of the detection molecules to allow for detection of the particular detection molecule in the presence or absence of the detection molecules for the other members of the biomarker panel.
• the specific binding partner can be a secondary antibody that recognizes the detection molecule that is a primary antibody.
• the specific binding partner can be a molecule that specifically binds to a group on the detection molecule such as, for example, a biotin group on the detection molecule.
• the term “subject” refers to any animal (e.g., a mammal), including, but not limited to, humans, non-human primates, rodents, and the like, which is to be the recipient of a particular treatment.
• the terms “subject” and “patient” are used interchangeably herein, such as but not limited to in reference to a human subject.
• the terms "subject suspected of having lung cancer” and “subject with potential lung cancer” include a subject that presents one or more symptoms indicative of lung cancer or is being screened for lung cancer (e.g., during a routine physical).
• a subject suspected of having lung cancer can also have one or more risk factors for developing lung cancer (e.g., a history of smoking, a history of exposure to carcinogens (including second hand smoke), a family history of cancer, genetic markers for lung cancer, a prior cancer, etc.).
• a subject suspected of having cancer has generally not been tested for cancer.
• a "subject suspected of having lung cancer” is an individual who has had a CT scan or other imaging study showing an indeterminate pulmonary nodule or other abnormality. The term further includes people who once had lung cancer (e.g., an individual in remission).
• the presently disclosed subject matter provides methods related in part to a panel of serum biomarkers useful for the detection, desirably early detection, of lung cancer and the management of patients with potential lung cancer.
• the panel of serum biomarkers provided herein in combination with indeterminate pulmonary nodule size, addresses certain limitations of early detection of tumors by CT screening alone or by biomarker screening alone. Indeterminate pulmonary nodules are a common radiographic finding and present a diagnostic dilemma. See Brandman et al. (201 1 ) and Weir et a]. (201 1 ). These anatomic abnormalities typically require further evaluation because of the concern for lung cancer. Patient work-up can depend on the clinical scenario and the size and morphology of the abnormality.
• biomarkers The concept of biomarkers is founded on the biological properties of cancer as a systemic disease. As a malignancy develops, it secretes proteins required for growth and metastasis and sheds cells into the circulation. The host responds by inducing changes in tissue architecture and vasculature in the microenvironment of the incipient tumor, as well as systemically mounting an immunological defense. This can include innate and adaptive responses with migration of inflammatory cells including macrophages, histiocytes and lymphocytes into the tumor, and the production of autoantibodies. See Petersen et al. (2006); Zhong et al. (2006); Welsh et al. (2005); and Condeelis et al. (2006). Thus, a combination of tumor-expressed and host response proteins, if identified, can be useful to develop a profile of cancer for clinical screening.
• Alpha-1 -antitrypsin has been associated with both the detection and etiology of lung cancer. See Liuiic et al. (2006) and Zelvvte et al. (2004). Transferrin, retinol binding protein (RBP), haptoglobin and AAT were previously identified as being differentially expressed serum proteins in patients with and without lung cancer. See PCT International Publication No. WO 2008/144034.
• Carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC) are two additional proteins whose serum levels are known to vary depending on lung cancer status. While each of these markers has been associated with cancer, they are independently insufficient to direct patient care.
• LCBA Lung Cancer Biomarker Assay
• LCBA data and nodule size data is statistically analyzed (e.g., by CART analysis or logistic regression analysis) and the results used to assign patients to a high or low-risk group.
• the risk designation can more efficiently guide further evaluation of the patients, rather than treating all patients with indeterminate pulmonary nodules or all patients with similar size pulmonary nodules with the same management plan.
• serum CEA levels and nodule size are assessed in combination to determine the risk for lung cancer.
• methods for assigning a subject with an indeterminate pulmonary nodule or abnormality to a group having a higher or lower probability of lung cancer.
• the methods comprise determining the level of CEA in a serum sample from the subject and assigning the subject to a group having a higher or lower probability of lung cancer based on the determined amount of CEA and based on the size of the indeterminate pulmonary nodule or abnormality.
• the methods comprise: determining the level of each of a panel of biomarkers in a serum sample from the subject with an indeterminate pulmonary nodule or abnormality, wherein the panel of biomarkers comprises at least two proteins that are differentially expressed in lung cancer patients; and assigning the subject to the group having a higher or lower probability of lung cancer based on the determined amount of each biomarker in the panel and based on the size of the indeterminate pulmonary nodule or abnormality.
• the panel of biomarkers includes at least two of the proteins AAT, CEA, and SCC.
• the panel of biomarkers includes CEA and AAT.
• the panel of biomarkers includes at least the three proteins AAT, CEA, and SCC. In some embodiments, the panel of biomarkers is AAT, CEA and SCC. In some embodiments, biomarkers other than or in addition to AAT, CEA, and SCC (e.g., including, but not limited to, RBP, transferrin, and haptoglobin) can be included in the biomarker panel.
• the presently disclosed subject matter is not limited to the panels of biomarkers described above. Any marker that correlates with lung cancer or the progression of lung cancer can be included in the biomarker panel provided herein, and is within the scope of the presently disclosed subject matter. Any suitable method can be utilized to identify additional lung cancer biomarkers suitable for use in the presently disclosed methods, including but not limited to, the methods described in PCT International Publication No. 2008/144034. For example, biomarkers that are known or identified as being up or down-regulated in lung cancer using methods known to those of ordinary skill in the art can be employed. Additional biomarkers can include one or more of polypeptides, small molecule metabolites, lipids, and nucleotide sequences. Markers for inclusion on a panel can be selected by screening for their predictive value using any suitable method, including but not limited to, those methods described in PCT International Publication No. 2008/144034, which is incorporated herein by reference in its entirety.
• the level of each of the presently disclosed panel of biomarkers can be determined in a variety of animal tissues.
• the biomarkers can be detected in animal tissue or bodily fluids.
• the biomarkers can be detected in bodily fluids including plasma, serum, whole blood, mucus, and/or urine.
• the biomarkers can be detected in serum.
• detecting biomarkers can include, but are not limited to, gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT- IR), and inductively coupled plasma mass spectrometry (ICP-MS).
• GC gas chromatography
• LC-MS liquid chromatography/mass spectroscopy
• GC-MS gas chromatography/mass spectroscopy
• NMR nuclear magnetic resonance
• MRI magnetic resonance imaging
• FT- IR Fourier Transform InfraRed
• ICP-MS inductively coupled plasma mass spectrometry
• mass spectrometry techniques include, but are not limited to, the use of magnetic-sector and double focusing instruments, transmission quadrapole instruments, quadrupole ion-trap instruments, time- of-flight instruments (TOF), Fourier transform ion cyclotron resonance instruments (FT-MS), and matrix-assisted laser desorption/ionization time-of- flight mass spectrometry (MALDI-TOF MS).
• TOF time- of-flight instruments
• FT-MS Fourier transform ion cyclotron resonance instruments
• MALDI-TOF MS matrix-assisted laser desorption/ionization time-of- flight mass spectrometry
• protein biomarkers can be detected using technologies well known to those of skill in the art such as gel electrophoresis, immunohistochemistry, and antibody binding. Methods for generating antibodies to a polypeptide of interest are well known to those of ordinary skill in the art.
• An antibody against a protein biomarker of the presently disclosed subject matter can be any monoclonal or polyclonal antibody, so long as it suitably recognizes the protein biomarker.
• antibodies are produced using the protein biomarker as the immunogen according to any conventional antibody or antiserum preparation process. The presently disclosed subject matter provides for the use of both monoclonal and polyclonal antibodies.
• a protein used herein as the immunogen is not limited to any particular type of immunogen.
• fragments of the protein biomarkers of the presently disclosed subject matter can be used as immunogens.
• the fragments can be obtained by any method including, but not limited to, expressing a fragment of the gene encoding the protein, enzymatic processing of the protein, chemical synthesis, and the like.
• the antibodies of the presently disclosed subject matter can be useful for detecting the protein biomarkers.
• antibody binding is detected by techniques known in the art (e.g., radioimmunoassay, ELISA (enzyme-linked immunosorbant assay), "sandwich” immunoassays, immunoradiometric assays, gel diffusion precipitation reactions, immunodiffusion assays, in situ immunoassays (e.g., using colloidal gold, enzyme or radioisotope labels, for example), Western blots, precipitation reactions, agglutination assays (e.g., gel agglutination assays, hemagglutination assays, etc.), complement fixation assays, immunofluorescence assays, protein A assays, and Immunoelectrophoresis assays, etc.
• radioimmunoassay e.g., ELISA (enzyme-linked immunosorbant assay), "sandwich” immunoassays
• the subject of, or provided in, the presently disclosed methods is typically a subject that has been determined to have an indeterminate pulmonary nodule or abnormality based upon an imaging study (e.g., a prior imaging study, such as a prior CT study).
• the size of the nodule or abnormality can be determined, or can have been determined, for example, from a prior imaging study, in accordance with any routinely available technique.
• the imaging study can have been performed for any reason.
• the subject can be a subject considered at risk for developing lung cancer based upon smoking history or other risk factors and who has undergone a screening CT study that found an indeterminate pulmonary nodule.
• the imaging can have been performed for reasons unrelated to lung cancer screening (e.g., pulmonary embolism, pneumonia, interstitial lung disease, chest pain, etc.).
• the subject is a human subject with an indeterminate pulmonary nodule or abnormality.
• the method comprises assigning the nodule or abnormality to a size category (e.g., small, intermediate, or large) based upon the size (e.g., the diameter) of the nodule or abnormality.
• the diameter can be the largest of the length, height, or width of the nodule or abnormality. The diameter can be determined based upon data extracted from the CT image used to originally determine the presence of the nodule or abnormality.
• nodules or abnormalities that have a diameter that is less than one centimeter are assigned to a small size category.
• nodules or abnormalities that have a diameter that is at least one centimeter, but less than three centimeters are assigned to an intermediate size category.
• nodules or abnormalities that have a diameter that is three centimeters or more are assigned to a large size category.
• the presently disclosed methods can comprise statistically analyzing the amounts of each biomarker and the nodule or abnormality size.
• the statistical analysis can comprise applying a predetermined algorithm to the determined amount of each biomarker in the panel and the size of the indeterminate pulmonary nodule or abnormality.
• the results of the algorithm can be employed to assign a subject to a group having a higher or lower probability of lung cancer.
• a variety of algorithms can be employed in the presently disclosed methods. The algorithms employed are not limited to those described in the Examples herein, but rather include algorithms as would be apparent to those of ordinary skill in the art upon a review of the instant disclosure.
• the algorithm employed can be a decision tree analysis.
• the algorithm employed can be a Classification and Regression Tree analysis (CART).
• the algorithm employed can be a logistic regression analysis or a weighted logistic regression analysis.
• the nodule or abnormality size or size category can be used to select the particular statistical treatment performed on data from a particular subject. For example, in some embodiments, a CART analysis algorithm is selected when the indeterminate nodule or abnormality has a diameter of three centimeters of more or is assigned to the large size category. In some embodiments, a logistic regression analysis algorithm is selected when the nodule or abnormality has a diameter than is less than three centimeters or has been assigned to the intermediate or small size category. In some embodiments, a weighted logistic regression analysis algorithm is selected when the nodule or abnormality has been assigned to the intermediate size category or has a diameter that is at least one centimeter but less than three centimeters.
• the subject is assigned to a high risk group for lung cancer.
• Subjects assigned to the high risk group are subjects who have greater than 60, 65, 70, 75, 80, 85, 90, or 95% chance of having lung cancer (e.g., of receiving a diagnosis of lung cancer in the future, based on, for example, histopathological confirmation).
• the subject is assigned to a low-risk group for lung cancer.
• Subjects assigned to the low risk group for lung cancer are those subjects who are believed to be unlikely (e.g., have a less than 40%, 35, 30, 25, 20, 15, 10, or less than 5% chance) of having cancer.
• a method for managing treatment of a subject with potential lung cancer.
• the method comprises: determining the level of CEA in a serum sample from the subject; assigning the subject to a group having a higher or lower probability of lung cancer based on the determined amount of CEA and based on the size of the indeterminate pulmonary nodule or abnormality; and managing the treatment of the subject with potential lung cancer based on the group to which the subject is assigned and based on the size of the indeterminate pulmonary nodule or abnormality.
• the method can comprise: determining the level of each of a panel of biomarkers in a serum sample from a subject with an indeterminate pulmonary nodule or abnormality; assigning the subject to a group having a higher or lower probability of lung cancer based on the determined amount of each biomarker in the panel and based on the size of the indeterminate pulmonary nodule or abnormality; and managing the treatment of the subject with potential lung cancer based on the group to which the subject is assigned and based on the size of the indeterminate pulmonary nodule or abnormality.
• the panel of biomarkers comprises at least two of the proteins AAT, CEA, and SCC.
• the panel of biomarkers includes CEA and AAT.
• the panel comprises at least AAT, CEA and SCC. In some embodiments, the panel is AAT, CEA, and SCC. In some embodiments, biomarkers other than or in addition to AAT, CEA, and SCC (e.g., including, but not limited to, RBP, transferrin, and haptoglobin) can be included in the biomarker panel. In some embodiments, the panel comprises at least two proteins that are differentially expressed in lung cancer patients.
• Managing treatment can comprise selecting appropriate time frames in which to schedule additional scans or studies, such as, but not limited to CT scans, biopsies, surgery, and PET scans, for the subject.
• Managing treatment can further comprise selecting an appropriate time frame in which to schedule a repeat assessment of biomarker levels or the assessment of additional biomarkers. For example, for subjects assigned to a low risk category, a future scan can be scheduled several months (e.g., 6, 7, 8, 9, 10, 1 1 , 12, 14, 16, 18 months) or one or more years in the future.
• Subjects assigned to a high risk category can be scheduled for an additional imaging scan or a more invasive screening procedure (e.g., a biopsy) within a shorter time frame (3 months, 2 months, 1 months, 4, 3, or 2 weeks or less), particularly if they have a large size nodule or abnormality. Assignment to a high risk category in conjugation with a large nodule or abnormality size can indicate a benefit to scheduling an immediate imaging scan or other procedure.
• a more invasive screening procedure e.g., a biopsy
• a subject with an intermediate size nodule or abnormality assigned to a low risk category can be scheduled for a follow-up CT scan in 3 to 6 months. In some embodiments, a subject with an intermediate size nodule or abnormality assigned to either a high or low risk category can be scheduled for a follow-up CT scan in 3 months.
• the presently disclosed methods are useful for screening patients with indeterminate pulmonary nodules or abnormalities for lung cancer, e.g., for the early detection of lung cancer, and for managing the treatment of patients with indeterminate pulmonary nodules or abnormalities.
• the combination of the panel of biomarkers and nodule or abnormality size can be useful for screening patients prior to additional imaging or other known methods for detecting lung tumors, to define patients at high risk or higher risk for lung cancer.
• a kit for measuring an amount of each member of a panel of biomarkers in a sample of the subject.
• the kit can comprise: i) detection molecules specific for each of the biomarkers in the panel (e.g., at least two of CEA, SCC, and AAT or at least any two proteins that are differentially expressed in lung cancer patients), and ii) directions for measuring the amount of each member of the panel of biomarkers.
• the kit can also include directions for using the determined biomarker levels in combination with nodule or abnormality size to determine the probability of lung cancer.
• detection molecule is used herein in its broadest sense to include any molecule that can bind with sufficient specificity to one of the members of the biomarker panel to allow for detection of the particular biomarker member in the presence or absence of the other members of the panel. To allow for detection can mean to determine the presence or absence of the particular biomarker member and, in some embodiments, can mean to determine the amount of the particular biomarker.
• Detection molecules can include antibodies and antibody fragments.
• the detection molecules comprise a conjugated detectable group.
• the detection molecules comprise antibodies specific for each of the protein biomarkers in the panel.
• Radioactive labels e.g., 35 S, 25 l, 131 l
• fluorescent labels e.g., enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), fluorescent labels (e.g., fluorescein) and so forth, in accordance with known techniques, as will be apparent to one skilled in the art upon review of the present disclosure.
• enzyme labels e.g., horseradish peroxidase, alkaline phosphatase
• fluorescent labels e.g., fluorescein
• direct detection methods are provided, such as, for example, wherein the detection molecule is a primary antibody specific for a member of the biomarker panel and detection is by using a label on the primary antibody.
• the detection molecule can be detected using an indirect method such as by detecting binding of a specific binding partner to the detection molecule.
• the specific binding partner can be any molecule that binds with sufficient specificity to the detection molecule to allow for detection of the particular detection molecule in the presence or absence of the detection molecules for the other members of the biomarker panel.
• the detection molecule is a primary antibody and the primary antibody can be detected by detecting binding of a secondary antibody or a reagent or other specific binding partner to the primary antibody.
• the specific binding partner can be a secondary antibody that recognizes the detection molecule that is a primary antibody.
• the specific binding partner can be a molecule that specifically binds to a group on the detection molecule such as, for example, a biotin group on the detection molecule.
• the binding partner can be labeled.
• the binding partner is a secondary antibody that can be labeled.
• indirect detection methods can involve a detection molecule that is an unlabeled primary antibody and a binding partner that is a labeled secondary antibody. This method can be more sensitive than direct detection methods due to signal amplification through more than one secondary antibody reaction with different antigenic sites on the primary antibody.
• the indirect detection method is an immunofluorescence method, wherein the secondary antibody can be labeled with a fluorescent dye such as FITC, rhodamine or Texas red.
• the indirect detection method is an immunoenzyme method, wherein the secondary antibody can be labeled with an enzyme such as peroxidase, alkaline phosphatase or glucose oxidase.
• an immunoassay can comprise antibodies specific for each of the members of the panel of protein biomarkers and an approach for producing a detectable signal.
• the antibodies can be immobilized on a support (such as a bead, plate or slide) in accordance with known techniques and contacted with a test sample in liquid phase. The support can then be separated from the liquid phase and either the support phase or the liquid phase can be examined for the detectable signal that is related to the presence of the protein biomarker.
• kits for detecting each of the members of the panel of biomarkers can comprise detection molecules, such as antibodies, specific for the protein biomarkers in the panel, the reagents necessary for producing a detectable signal as described above and buffers.
• the kit can contain all of the components necessary to perform a detection assay, including all controls, directions for performing assays, and any necessary software for analysis of the data of the presently disclosed methods and presentation of results. Indeed, the presently disclosed methods and/or kits can be employed using a suitably programmed computer, in some aspects.
• the detection kit can comprise a detection molecule that is an antibody or antibody fragment that specifically binds to a protein biomarker in the panel immobilized on a solid support, and a second antibody or antibody fragment specific for the first antibody or antibody fragment conjugated to a detectable group.
• the kit can also include ancillary reagents such as buffering agents and protein stabilizing agents, and can include (where necessary) other members of the detectable signal-producing system of which the detectable group is a part (e.g., enzyme substrates); agents for reducing background interference in a test; control reagents; apparatus for conducting a test, and the like, as will be apparent to those skilled in the art upon a review of the instant disclosure.
• ancillary reagents such as buffering agents and protein stabilizing agents
• other members of the detectable signal-producing system of which the detectable group is a part e.g., enzyme substrates
• agents for reducing background interference in a test e.g., enzyme substrates
• control reagents e.g., apparatus for conducting a test, and the like
• the detection kit can comprise antibodies or antibody fragments specific for each of the protein biomarkers in the panel, and a specific binding partner for each of the antibodies that is conjugated to a detectable group.
• Ancillary agents as described above can likewise be included.
• the test kit can be packaged in any suitable manner, typically with all groups in a single container along with a sheet or printed instructions for carrying out the test.
• the detection assay for the biomarker panels can be automated.
• Methods for the automation of immunoassays include those described in U.S. Pat. Nos. 5,885,530, 4,981 ,785, 6,159,750, and 5,358,691 , each of which is herein incorporated by reference.
• analysis of the biomarker data in combination with nodule size and presentation of results can also be automated.
• a clinician can access the test results using any suitable approach or device.
• a clinician need not understand the raw data, as the data can be presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information to optimize care of the subject.
• the presently disclosed subject matter provides any method, system, and/or apparatus capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information providers, medical personnel, and subjects.
• LCBA Lung Cancer Biomarker Assay
• CEA carcinoembryonic antigen
• AAT alpha-1 -antitrypsin
• SCC squamous cell carcinoma antigen
• the SCC assay was run in duplicate, and the CEA and AAT assays were run on automated analyzers in singlicate.
• All patients enrolled in the validation study had a CT study of the thorax, performed for a variety of indications including the following: possible respiratory neoplasm, pulmonary nodule, interstitial lung disease, pulmonary embolism, pneumonia, coronary artery scoring, or chest pain.
• CT studies were performed using multiple contiguous 5 mm sequential axial images through the thorax. Typically, the size of the nodule or focal abnormality was extracted from the official documented clinical CT examination report. In the validation study, the tumor size from 18 patients (4%) was determined by a pathology report. All patients with lung cancer had histological confirmation and pathological staging. All patients with a benign abnormality had histological diagnosis, CT resolution or 2 year stability of the lesion, or clinical observation without evidence of lung cancer. EXAMPLE 2
• Serum samples were selected from an Institutional Review Board (IRB)-approved repository. The samples were all collected, processed, and stored in a similar fashion. Serum samples were selected from 298 patients with a confirmed diagnosis of lung cancer. In addition, 21 1 serum samples were selected from age, gender, and smoking history- matched individuals without cancer who met the following eligibility criteria: 1) no prior history of lung cancer; 2) no currently known extra-thoracic malignancy, and 3) a CT of the thorax with a pulmonary nodule. The distribution of benign and malignant nodules reflected the expected proportion of cases. See Figure 1A. Table 1 below shows the patient demographics for the training study.
• Training set data were analyzed using both classification and regression tree (CART) methodology and logistic regression.
• CART classification and regression tree
• the CART model (see Figure 2) was generated using the Gini index that favors even splits with a minimum size of 5 for terminal nodes, and 10 for parent nodes.
• Ten-fold cross-validation was used in generating this model.
• the sensitivity, specificity, and positive and negative predictive values for the training set were 90%, 82%, 88%, and 86%, respectively.
• This algorithm for the standard model uses the standard 0.50 as a decision point. A calculated probability of 0.50 or higher is considered to be cancer and a probability ⁇ 0.50 is considered to be no cancer.
• the sensitivity, specificity, and positive and negative predictive values for the standard model training set were 81 %, 84%, 88%, and 76%, respectively.
• the sensitivity weighted model also involved the same four data elements (nodule diameter, CEA, SCC and AAT results). This algorithm was designed to increase sensitivity and uses a decision point of 0.30. A calculated probability of 0.30 or higher is considered to be cancer and a probability ⁇ 0.30 is considered to be no cancer.
• the sensitivity, specificity, and positive and negative predictive values for the weighted model training set were 94%, 63%, 78%, and 89%, respectively.
• Table 4 shows the sensitivity, specificity, and positive and negative predictive value for each of the nodule size categories in the training set. Table 4. Logistic regression (LR) analysis of the training set according to size categories
• Patient Selection Patients with an indeterminate pulmonary nodule or focal pulmonary opacity detected on thoracic CT (performed for any indication) were enrolled in a validation study. All patients met the same eligibility criteria as for the training set. There were 203 patients with lung cancer and 196 patients without cancer. Patient demographics for the validation study are shown in Table 5. Serum was collected from each subject and stored at -80°C. After enrollment was complete, the samples were thawed for the first time and 500 ⁇ aliquots were placed in de-identified tubes with randomly assigned unique study identification numbers. Table 5. Patient Demographics and Clinical Profiles for the Validation Set
• the independent validation study was designed to test the accuracy of the CART and logistic regression models developed from the training set. Each model assigned each subject a "cancer" (high-risk group) or "no cancer” (low-risk group) designation. Following classification of all patients, the diagnosis was revealed and the sensitivity, specificity, and positive and negative predictive values for each model were determined.
• this validation study To power the validation study, a statistical model was developed that predicts the presence/absence of cancer among patients with a pulmonary nodule. The goal of this validation study was to assess the classification properties of the model within an independent blinded dataset. More specifically, this validation study determined whether the sensitivity and specificity of the derived classification algorithm is adequate for assessing the presence/absence of cancer within a patient group representative of all patients with a pulmonary nodule or focal pulmonary abnormality.
• the probability that a cancerous nodule is diagnosed as cancerous without the benefit of a pathology assessment is estimated to be 50%. If the serum based classification algorithm increased that probability to 75%, then it would be considered potentially clinically useful in the diagnosis of cancer in patients with a pulmonary nodule. If the classification algorithm increased the probability of detecting a cancerous tumor to 60%, it would not be worthy of clinical use. Therefore, the statistical hypothesis that would be tested for sensitivity is: H 0 : p ⁇ 0.6 vs. ⁇ . p>0.75 where p is the proportion of patients with tumors known to have cancerous nodules who are classified as having cancer according to the classification algorithm. A similar argument can be made for the determination of benign lesions when a nodule is truly benign.
• the statistical hypothesis that would be tested for specificity is: H 0 : q ⁇ 0.6 vs. H . q>0.75 where q is the proportion of patients known to have a benign pulmonary nodule who are classified as having benign disease. Assuming at least 384 patients are included in this validation study, then approximately 192 nodules will be cancerous and 192 nodules will be benign. With these sample sizes, the hypotheses for sensitivity and specificity as described above can be conducted at the 0.1 level of significance with >99% power.
• Characteristics of study participants in the independent validation study As described in Example 3, 399 patients fulfilled the eligibility criteria and were enrolled in the independent validation study. The diagnosis of primary lung cancer was established in 203 patients (50.8%) and a benign abnormality in 196 (49.2%). The distribution of benign and malignant nodules according to lesion size (small, ⁇ 1 cm; intermediate, 1 - ⁇ 3 cm; and large, > 3 cm) approximately reflected the expected proportion of cases. See Figure B.
• LCBA data and nodule size were used to assign a "cancer" (high- risk) or "no cancer” (low-risk) diagnosis to each patient using the two logistic regression models derived from the training set.
• the sensitivity, specificity, and positive and negative predictive values for determining which patient had lung cancer in the validation study were 80%, 89%, 89%, and 81 %, respectively for the logistic regression standard model (LR-N, 0.5 Threshold), and 92%, 74%, 79%, and 90%, respectively, for the logistic regression weighted model (LR-W, 0.3 Threshold).
• the sensitivity and specificity according to small, intermediate, and large nodule size categories for each model is shown in Table 6.
• management of patients can begin with stratification according to nodule size categories ⁇ i.e., small, intermediate, and large), and further evaluation can be determined by integrating biomarker results and the assignment to a risk group.
• Table 7 provides recommendations for the follow-up and management of patients with indeterminate pulmonary nodules.
• the small nodule category ( ⁇ 1 cm diameter lesions), the prevalence of lung cancer is low ( ⁇ 5% of all nodules).
• the standard model would place approximately 3% of individuals in a high-risk group, and would estimate that approximately 25% will eventually be diagnosed with lung cancer.
• a follow-up CT within 6 months is appropriate.
• the majority of patients in the small nodule size category will be assigned to the low-risk group.
• the standard model estimates that only 2% of individuals in the low-risk group will ever be diagnosed with lung cancer. These patients do not need as close surveillance as those in the high-risk group, and a follow-up CT in 9-12 months can be adequate. In those few individuals in the low-risk group that eventually demonstrate nodule growth and are diagnosed with lung cancer, the risk of cancer progression to more advanced stage disease during the observation period appears low.
• the use of a weighted logistic regression model appears appropriate, as it minimizes the number of lung cancer patients assigned to the low-risk category. Eighty-six percent (86%) of all lung cancers in this size category were placed in the high-risk group. 71 % of patients in this high risk category were diagnosed with lung cancer. Individuals assigned to this category could have an immediate or close (within 3 months) follow-up FDG-PET or biopsy. Individuals assigned to the low-risk group can have a follow-up in 3-6 months to assess for growth, with the understanding that approximately one third of individuals in this group will eventually be diagnosed with lung cancer. Alternatively, an immediate FDG-PET study could be performed.
• Patz EF Lowe VJ, Hoffman JM, et al. Focal pulmonary abnormalities: Evaluation with F-18 fluorodeoxyflucose PET scanning. Radiology; 88: 487- 490, 1993.
• Patz EF, Jr. Campa MJ, Gottlin EB, Kusmartseva I, Guan XR, Herndon JE, 2nd. Panel of serum biomarkers for the diagnosis of lung cancer. J Clin Oncol; 25(35): 5578-83, 2007.

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