Lung cancer detection and treatment methods

Abstract

A method is provided to identify individuals at high risk of death from lung cancer.

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 505,148, filed May 31, 2023, which is incorporated herein by reference as if its entire disclosure were described herein.

[0002] This invention was made with government support under CA194733, granted by the National Institutes of Health. The government has certain rights in this invention. [Background technology]

[0003] Lung cancer is the most prevalent cancer in the United States, with a five-year survival rate of less than 15%. In recent years, lung cancer treatment has begun to shift from limited options such as radiation, folate metabolism, platinum-based drugs, and taxane-based drugs to more targeted therapies that require histological characterization of the tumor and / or the presence or absence of key biomarkers or therapeutic target proteins.

[0004] Data from the National Lung Screening Trial (NLST) suggests that annual screening of high-risk current and former smokers using low-dose computed tomography (LDCT) chest scans could reduce lung cancer mortality by 20%. In 2021, the United States Preventive Service Task Force (USPSTF) expanded eligibility for LDCT screening, and now recommends annual lung cancer screening using LDCT for adults aged 50–80 with a smoking history of more than 20 pack-years, and who are currently smoking or have quit smoking within the past 15 years. However, there are several negative aspects associated with CT screening in terms of incidence, including overdiagnosis, false positives, overtreatment, and financial costs.

[0005] Numerous studies on lung cancer risk prediction have explored the potential benefits of supplementing USPSTF screening criteria with risk-based models when identifying targets for CT screening. For example, a recent study estimated that 20% of additional lung cancer deaths could be avoided by using screening criteria based on individual risk assessments. The information required to utilize risk prediction tools is readily available to general practitioners, or can be self-assessed using online risk calculators—potentially enabling future lung cancer screening programs that can implement such tools for evaluating screening eligibility.

[0006] Such tools would be risk-based, individual-level screening criteria that accurately estimate the risk of lung cancer in a given subject within the near future (e.g., 1-3 years). Several risk prediction models, PLCO, rely on demographic data (age, sex, etc.) and risk factor data from questionnaires. m2012 The Liverpool Lung Project (LLP) has also been published. Estimated levels of protein biomarkers have also been found to be useful predictors of the risk of developing lung cancer. A novel blood-based four-marker protein panel comprising or consisting of prosurfactant protein B (pro-SFTPB), mucin 16 (CA125), carcinoembryonic antigen (CEA), and cytokeratin-19 fragment (CYFRA21-1) is described in U.S. Patent Application No. 16 / 484,177, the entire disclosure of which is incorporated herein by reference. m2012 The use of this panel in conjunction with other methods has been shown to significantly improve lung cancer risk assessment compared to previous and current USPSFT criteria for lung cancer screening. There is evidence that this can be further improved by identifying and using small molecule metabolites as cancer biomarkers, as cellular and systemic metabolic adaptations occur from the earliest stages of cancer development.

[0007] Therefore, there is a need for methods or tests that can help detect lung cancer deaths.

Summary of the Invention

Means for Solving the Problems

[0008] A method for predicting the prognosis of an individual with lung cancer, comprising: PLCO m2012 Determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, wherein the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample collected from a patient are measured from the biomarker score; and Provided herein is a method wherein the combined model score indicates the prognosis of an individual with respect to progression-free survival and overall survival.

[0009] A method for predicting disease outcome in an individual with lung cancer, comprising: PLCO m2012 Determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, wherein the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample collected from a patient are measured from the biomarker score; and Identifying an individual as having an increased risk of a poor disease outcome if the combined model score exceeds a defined risk threshold; A method including the above steps is also provided.

[0010] A method for identifying an individual with a high risk of lung cancer death, comprising: PLCO m2012 Determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, wherein the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample collected from a patient are measured from the biomarker score; and A process for identifying individuals at high risk of death from lung cancer when their combined model score exceeds a defined risk threshold; Methods including this are also provided. [Brief explanation of the drawing]

[0011] [Figure 1] This study demonstrates the predictive performance of 4MP, PLCOm2012, and the combined 4MP+PLCOm2012 model for predicting lung cancer-specific mortality. Serum samples from cases collected within one year of diagnosis, as well as all non-case serum samples, were considered. Nodes indicate corresponding sensitivity and specificity based on USPSTF2013 or 2021 criteria. [Figure 2] This paper demonstrates the predictive performance of 4MP, PLCOm2012, and the combined 4MP+PLCOm2012 model for predicting lung cancer-specific one-year mortality. [Figure 3] This paper demonstrates the predictive performance of 4MP, PLCOm2012, and the combined 4MP+PLCOm2012 model for predicting lung cancer-specific 6-year mortality. [Figure 4] This paper demonstrates the predictive performance of 4MP, PLCOm2012, and a combined 4MP+PLCOm2012 model for predicting lung cancer-specific mortality among ever-smokers with a smoking history of <10 years. [Figure 5] This paper demonstrates the predictive performance of 4MP, PLCOm2012, and a combined 4MP+PLCOm2012 model for predicting lung cancer-specific mortality among smokers with a smoking history of ≥10 years. [Figure 6] This paper demonstrates the predictive performance of 4MP, PLCOm2012, and the combined 4MP+PLCOm2012 model for predicting lung cancer-specific mortality in unique and non-unique cases. [Figure 7] This plot shows the cumulative incidence of lung cancer deaths for individuals who were test-positive and test-negative based on a 6-year risk threshold of 1.0% and 1.7% or less (4MP+PLCOm2012 score). The analysis is based on cases diagnosed within one year of blood collection and non-case participants with a smoking history of 10 PYS or more. [Figure 8] This plot shows the cumulative incidence of lung cancer deaths among all asymptotic individuals who were both test-positive and test-negative based on a 4MP score with a 6-year risk threshold of 1.0%. The analysis is based on cases with a smoking history of 10 PYS or more, and all lung cancer cases are within one year of clinical diagnosis of cancer. [Figure 9] This plot shows the cumulative incidence of lung cancer deaths among all asymptotic individuals who were test positive and test negative, based on a 4MP score with a 6-year risk threshold of 1.7%. [Figure 10] This plots the cumulative incidence of lung cancer deaths among all asymptotic individuals who were test positive and test negative, based on the PLCOm2012 score with a 6-year risk threshold of 1%. [Figure 11] This plot shows the cumulative incidence of lung cancer deaths among all asymptotic individuals who were test positive and test negative, based on the PLCOm2012 score with a 6-year risk threshold of 1.7%. [Figure 12] This plot shows the cumulative incidence of lung cancer and non-lung cancer deaths diagnosed within one year of blood collection, based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1%. The analysis is based on cases with a smoking history of 10 PYS or more. [Figure 13] This plot shows the cumulative incidence of lung cancer and non-lung cancer deaths diagnosed within one year of blood collection, based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1.7%. [Figure 14] This plot shows the cumulative incidence of lung cancer deaths among all asymptotic individuals who were test positive and test negative based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1%. The analysis is based on cases with a smoking history of 10 PYS or more, and all lung cancer cases are within 1 to 6 years of clinical diagnosis of cancer. [Figure 15]This plot shows the cumulative incidence of lung cancer deaths among all asymptotic individuals who were test positive and test negative based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1.7%. The analysis is based on cases with a smoking history of 10 PYS or more, and all lung cancer cases are within 1 to 6 years of clinical diagnosis of cancer. [Figure 16] This plot shows the cumulative incidence of lung cancer and non-lung cancer deaths diagnosed within 1–6 years of blood collection, based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1.7%. The analysis is based on cases with a smoking history of 10 PYS or more. [Figure 17] This plot shows the cumulative incidence of lung cancer and non-lung cancer deaths diagnosed within 1–6 years of blood collection, based on the 4MP+PLCOm2012 score with a 6-year risk threshold of 1.0%. The analysis is based on cases with a smoking history of 10 PYS or more. [Modes for carrying out the invention]

[0012] A method for predicting the prognosis of an individual with lung cancer, PLCO m2012 A step of determining a combined model score using logistic regression analysis along with a risk model score and a biomarker score, comprising measuring the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample taken from a patient, based on the biomarker score. This specification provides a method in which the combined model score indicates an individual's prognosis with respect to progression-free survival and overall survival.

[0013] In some embodiments, if the combined model score exceeds a predefined risk threshold, the individual is predicted to have a poor survival prognosis for lung cancer.

[0014] A method for predicting disease outcomes in individuals with lung cancer, PLCO m2012A step of determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, the step of measuring the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample collected from a patient from the biomarker score; and A step of identifying an individual as having an increased risk of a bad disease outcome when the combined model score exceeds a defined risk threshold; A method including the above is also provided.

[0015] A method of identifying an individual at high risk of lung cancer death, PLCO m2012 A step of determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, the step of measuring the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample collected from a patient from the biomarker score; and A step of identifying an individual as having a high risk of lung cancer death when the combined model score exceeds a defined risk threshold; A method including the above is also provided.

[0016] In some embodiments, PLCO m2012 The risk model score is based on baseline questionnaire information including age, race or ethnic group, education level, body mass index, chronic obstructive pulmonary disease, personal history of cancer, family history of lung cancer and smoking status (current vs. previous), intensity, duration, and smoking cessation period.

[0017] In some embodiments, the combined model score is given by the equation: -11.836 + 1.6160 * (0.4730 * log[CA125] + 0.6531 * log[CEA] + 0.2612 * log[CYFRA21-1] + 0.9238 * log[Pro-SFTPB]) + 0.9861 * (PLCOm2012 It is calculated using the score.

[0018] In some embodiments, the predefined risk threshold is a 6-year risk threshold of 1.0%.

[0019] In some embodiments, a combined model score greater than -4.595 is considered a positive test result.

[0020] In some embodiments, the predefined risk threshold is a 6-year risk threshold of 1.7%.

[0021] In some embodiments, a combined model score greater than -4.057 is considered a positive test result.

[0022] In some embodiments, the individual is asymptomatic.

[0023] In some embodiments, individuals are at high risk.

[0024] In some embodiments, the method described herein further includes calculating an individual's lifespan.

[0025] In some embodiments, the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in biological samples collected from individuals are determined by immunoassay.

[0026] In some embodiments, the biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in biological samples collected from individuals each produce a detectable signal.

[0027] In some embodiments, detectable signals can be detected by spectroscopy.

[0028] In some embodiments, the spectroscopic method is selected from ultraviolet-visible spectroscopy, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, gas chromatography, mass spectrometry (GC-MS), liquid chromatography-mass spectroscopy (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating-frame nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC-TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectroscopy.

[0029] In some embodiments, the spectroscopy method is mass spectrometry.

[0030] In some embodiments, the mass spectrometry method is LC-TOF-MS.

[0031] In some embodiments, the lung cancer is in an early stage (e.g., stage I or II).

[0032] In some embodiments, the lung cancer is in an advanced stage (e.g., stage III or IV).

[0033] In some embodiments, the individual has a smoking history of 10 pack years or more.

[0034] In some embodiments, the individuals are between 50 and 80 years old.

[0035] In some embodiments, the AUC of the method is higher than the AUC of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

[0036] In some embodiments, the AUC exceeds 0.85.

[0037] In some embodiments, the AUC is 0.86 to 0.90.

[0038] In some embodiments, the AUC is approximately 0.88.

[0039] In some embodiments, the sensitivity and specificity values ​​of this method at a≧1.7% / 6-year risk threshold are higher than the sensitivity and specificity values ​​of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

[0040] In some embodiments, the sensitivity exceeds 0.80 and the specificity exceeds 0.65.

[0041] In some embodiments, the sensitivity is 0.82 to 0.91 and the specificity is 0.70 to 0.72.

[0042] In some embodiments, the sensitivity is approximately 0.85 and the specificity is approximately 0.71.

[0043] In some embodiments, the sensitivity and specificity values ​​of this method at a≧1.0% / 6-year risk threshold are higher than the sensitivity and specificity values ​​of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

[0044] In some embodiments, the sensitivity exceeds 0.85 and the specificity exceeds 0.55.

[0045] In some embodiments, the sensitivity is 0.87 to 0.94 and the specificity is 0.56 to 0.59.

[0046] In some embodiments, the sensitivity is approximately 0.90 and the specificity is approximately 0.58.

[0047] In some embodiments, the individual is subsequently designated for further lung cancer screening or treatment.

[0048] In some embodiments, screening is selected from endoscopic ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT) scans.

[0049] In some embodiments, screening is performed annually.

[0050] In some embodiments, screening is performed every six months.

[0051] In some embodiments, treatment is selected from surgery, chemotherapy, immunotherapy, radiotherapy, targeted therapy, or a combination thereof.

[0052] definition As used herein, the following terms have the meanings indicated.

[0053] When a range of values ​​is disclosed and the notation "from n1 to n2" or "between n1 and n2" (where n1 and n2 are numbers) is used, unless otherwise specified, this notation is intended to include the number itself and the range between it. This range can be an integer or continuous, between and including the endpoints. For example, since carbon is an integer unit, the range "2 to 6 carbons" is intended to include 2, 3, 4, 5, and 6 carbons. For comparison, the range "1 to 3 μM (micromoles)" is intended to include all values ​​between 1 μM, 3 μM, and any number of significant figures (e.g., 1.255 μM, 2.1 μM, 2.9999 μM, etc.).

[0054] As used herein, the term “approximately” is intended to modify the numerical value it modifies, indicating such value as a variable within a certain range. If no specific range is given, such as error range or standard deviation, or if the mean is not shown in a chart or table of data, the term “approximately” should be understood to mean the larger of the following: the range encompassing the stated value, the range included by rounding up or down to the number of significant figures, and the range encompassing ±20% of the stated value.

[0055] As used herein, “lung cancer” means a malignant tumor of the lung characterized by abnormal cell proliferation, in which the proliferation of its cells exceeds and is in harmony with the proliferation of the surrounding normal tissue. In some embodiments, lung cancer may vary in severity and be represented by stages I through IV. In some embodiments, lung cancer may be in an early stage (e.g., stage I or II) or an advanced stage (e.g., stage III or IV).

[0056] As used herein, the terms “subject” or “patient” mean a mammal, preferably a human, that is preferably classified as lung cancer-positive or lung cancer-negative and for which further treatment may be offered.

[0057] As used herein, “healthy” means an individual in whom no evidence of lung cancer is found, i.e., the individual does not have lung cancer. Such an individual may be classified as “lung cancer negative” or having healthy lungs or normal, unimpaired lung function. A healthy patient or subject may not have symptoms of lung cancer but may have benign lung nodules or pulmonary masses, i.e., a combination of sebaceous adenoma and cyst, or other medical conditions such as non-cancerous lung conditions or chronic obstructive pulmonary disease (COPD). In some embodiments, a healthy patient or subject may be used for comparison with diseased or suspected diseased samples for determining lung cancer in a patient or group of patients.

[0058] As used herein, “to treat,” “treatment,” etc., means providing therapy to an individual who is already exhibiting or has previously exhibited at least one symptom of a disease or condition. For example, “treatment” may include alleviating, reducing, or improving a symptom or condition; preventing further symptoms; improving the underlying metabolic cause of a symptom; inhibiting a symptom or condition, such as preventing the onset of a symptom or condition; reducing a symptom or condition; causing regression of a symptom or condition; reducing a condition caused by a symptom or condition; or stopping a symptom or condition. For example, the term “to treat” in relation to a disease means reducing the severity of one or more symptoms associated with that particular disease. Therefore, treatment of a disease does not necessarily mean reducing the severity of all symptoms associated with the disease, nor does it necessarily mean a complete reduction in the severity of one or more symptoms associated with the disease. In relation to this disclosure, the term may also mean the administration of pharmacological substances or formulations, or the performance of non-pharmacological methods such as radiotherapy and surgery, but not limited to these. The pharmacological substances used herein include, but are not limited to, anticancer agents, such as chemotherapeutic agents, polyamine inhibitors, hormone therapies, and targeted therapies.Examples of chemotherapy agents for lung cancer include paclitaxel / Taxol (e.g., albumin-bound paclitaxel or nab-paclitaxel, trade name Abraxane®), erlotinib (Tarceva®, etc.), afatinib (Gilotrif®), gefitinib (Iressa®), bevacizumab (Avastin®), gemcitabine (Gemzar®), crizotinib (Xalkori®), ceritinib (Zykadia®), cisplatin / platinol, carboplatin (Paraplatin®), and docetaxel (Taxot). Examples include ere(registered trademark)), pemetrexed (Alimta(registered trademark)), and vinorelbine (Navelbine(registered trademark)); as well as combination chemotherapy therapies, such as cisplatin + paclitaxel, TIP (paclitaxel / taxol, ifosfamide, and cisplatin / platinol), VeIP (vinblastine, ifosfamide, and cisplatin / platinol), VIP (etoposide / VP-16, ifosfamide, and cisplatin / platinol), VAC (vincristine, dactinomycin, and cyclophosphamide), and PEB (cisplatin / platinol, etoposide, and bleomycin). "Pharmacological substances" and "anti-cancer therapies" may also include substances used in immunotherapy, such as checkpoint inhibitors. Treatments include numerous pharmacological substances, or, but are not limited to, numerous treatment methods such as surgery and chemotherapy.

[0059] As used herein, “quantity” or “level” means a generally quantifiable measurement of a biomarker described herein, which enables comparison of the marker between samples and / or against a control sample. In some embodiments, quantity or level is quantifiable and means the level of a particular marker in a biological sample (e.g., blood, serum, urine, etc.) determined by an experimental method or test such as immunoassay (e.g., antibody), mass spectrometry, or liquid chromatography. In some embodiments, the marker may be present in the sample in increased or decreased amounts. Comparison of markers may be based on direct measurement of the levels of the biomarkers described herein (e.g., by protein quantification or gene expression analysis), or on measurement of, for example, a reporter molecule, a biomarker-receptor complex, a biomarker-relay receptor complex, etc.

[0060] As used herein, the term “high” means a biomarker level or model score in a given subject that is greater than the same biomarker level or model score in a given set of healthy patients or subjects. In some embodiments, high PLCO m2012 The model score is 0.00948 or higher. In some embodiments, high PLCO m2012 The model score is 0.016082 or higher.

[0061] As used herein, the term “hazard ratio” refers to a measure of how frequently a particular event occurs in one group over time compared to how frequently it occurred in another group. Hazard ratios are often used in clinical trials to estimate the survival rate at any given time in a group of patients receiving a particular treatment compared to a control group receiving a different treatment or a placebo. Hazard is defined as the slope of the survival curve—a measure of how quickly subjects die. A hazard ratio of 1 means there is no difference in survival rates between the two groups. A hazard ratio greater than or less than 1 means that one of the groups has a better survival rate. A hazard ratio of 2.0 means that the mortality rate in one treatment group is twice as high as the rate in the other group.

[0062] As used herein, “regression” means a statistical method that can assign predictive values ​​to underlying traits of a sample based on observable traits (a set of observable traits) of the sample. In some embodiments, the traits are not directly observable. For example, in the regression methods used herein, the qualitative or quantitative outcomes of a particular biomarker test, or a set of biomarker tests, for a particular subject are linked to the likelihood that the subject is lung cancer positive.

[0063] As used herein, the term “logistic regression analysis” means a regression method in which the assignment of predictions from a model may have one of several allowable discrete values. For example, a logistic regression analysis model used herein may assign a prediction of lung cancer positive or lung cancer negative to any particular subject.

[0064] As used herein, the term “biomarker score” refers to a numerical score of a given biomarker measured in a sample from a subject. The biomarker score is calculated by normalizing or weighting the measurement levels using fixed coefficients determined by a statistical method for a given biomarker panel. The biomarker score is used as a component in calculating the subject’s risk score. Higher biomarker scores carry greater weight in the risk score calculation and may indicate a higher risk of lung cancer in the subject.

[0065] As used herein, the term “risk score” means a single numerical value representing the risk of lung cancer in an asymptomatic human subject compared to the known prevalence of lung cancer in a disease cohort. The risk score is calculated by summing parameters of a statistical method derived from the subject with respect to a given panel of biomarkers, which may take the form of a biomarker score, a statistical model score, or a model constant. A higher risk score correlates with a higher risk of lung cancer in the subject. The risk score is derived empirically and varies depending on the data, the cohort of the subject population, the type of lung cancer, the biomarkers selected, and environmental factors, etc. In certain embodiments, the risk score calculated for a human subject is the sum of biomarker scores obtained from the subject. In certain embodiments, the risk score calculated for a human subject is the sum of biomarker scores obtained from the subject and one or more additional model constants. In certain embodiments, the risk score calculated for a human subject is the sum of biomarker scores obtained from the subject, a normalized score from one or more additional statistical models based on the subject’s risk factors, and one or more additional model constants.

[0066] As used herein, the term “risk profile” refers to an assessment of a patient’s risk score compared to a large number of patients assessed using the same model, where the patient is placed into an appropriate risk group based on a predetermined score threshold. The score threshold is derived empirically and varies depending on the data, the cohort of the target population, the type of lung cancer, the biomarkers selected, and environmental factors, etc. In certain embodiments, a patient’s risk score exceeds the score threshold, and based on their risk profile, the patient is classified as at risk of lung cancer (“positive”). In certain embodiments, a patient’s risk profile is below the score threshold, and the patient is classified as not at risk of lung cancer (“negative”). In some embodiments, the score threshold is 0.005, or 0.5%, or higher. In some embodiments, the score threshold is 0.01, or 1%, or higher. In some embodiments, the score threshold is 0.05, or 5%, or higher. In some embodiments, the score threshold is 0.1, or 10%, or higher.

[0067] As used herein, the terms “cutoff” or “cutoff point” mean mathematical values ​​associated with specific statistical methods that may be used to assign a classification of a subject as lung cancer positive or lung cancer negative based on the subject’s biomarker score.

[0068] As used herein, when a value exceeding or below a cutoff value is described as "specific to lung cancer," it means that the subject from which the value is obtained through the analysis of that sample has lung cancer or is at risk of developing lung cancer.

[0069] As used herein, “use” of a marker for the diagnosis of lung cancer means the quantification of the level or amount of one or more markers described herein in a biological sample. Quantification may be performed using any known method or technique in the art or described herein. In some embodiments, markers may be used as a panel for statistical comparison with other samples, or may be combined with each other.

[0070] In some embodiments, the panel includes markers pro-SFTPB, CA125, CEA, CYFRA21-1, and PLCO. m2012 When using model scores, the AUC (95% CI) can be 0.84 or higher, for example, approximately 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, etc.

[0071] In some embodiments, analysis of any of the marker panels described herein for lung cancer diagnosis using a fixed coefficient resulted in the differentiation of early-stage lung cancer with an AUC (95% CI) of approximately 0.55 to approximately 0.88, e.g., approximately 0.55, approximately 0.56, approximately 0.57, approximately 0.58, approximately 0.59, approximately 0.60, approximately 0.61, approximately 0.62, approximately 0.63, approximately 0. The values ​​can be approximately 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, etc. In some embodiments, analysis of these markers using fixed coefficients can result in an AUC (95% CI) of 0.86, which can distinguish early-stage lung cancer.

[0072] As used herein, subjects at “risk for lung cancer” are subjects who have not yet demonstrated overt symptoms of lung cancer, but who exhibit levels of biomarkers indicating that they have lung cancer or are likely to develop it in the near future. Subjects with lung cancer, or suspected of having lung cancer, may be treated for cancer or suspected cancer.

[0073] As used herein, the term “classification” means assigning a subject to either be at risk of lung cancer or not at risk of lung cancer based on the results of a biomarker score, risk score, or risk profile obtained for the subject.

[0074] As used herein, the term “Wilcoxon rank-sum test,” also known as the Mann-Whitney U test, Mann-Whitney-Wilcoxon test, or Wilcoxon-Mann-Whitney test, refers to a specific statistical method used to compare two populations. For example, the test can be used herein to associate observable features, particularly biomarker levels, with the presence or risk of lung cancer in a particular population of subjects.

[0075] As used herein, the term “sensitivity” refers to the ability of an assay to accurately identify subjects with a disease (i.e., the true positive rate) in the context of various biochemical assays. In comparison, as used herein, the term “specificity” refers to the ability of an assay to accurately identify subjects without a disease (i.e., the true negative rate) in the context of various biochemical assays. Sensitivity and specificity are statistical measures of the performance (i.e., classification function) of a binary classification test. Sensitivity quantifies the avoidance of false negatives, while specificity quantifies the avoidance of false positives.

[0076] As used herein, “sample” means a test substance to be tested for the presence, level, or concentration of a biomarker as described herein. The sample may be any substance appropriate in accordance with this disclosure, including but not limited to blood, serum, plasma, or any part thereof.

[0077] As used herein, “metabolites” means small molecules that are intermediates and / or products of cellular metabolism. Metabolites can exert various functions in cells, such as structural, signaling, stimulating, and / or inhibitory effects on enzymes. In some embodiments, metabolites may be non-protein, plasma-derived metabolite markers, such as, but not limited to, DAS, arginine, and creatine riboside.

[0078] As used herein, the term "ROC" refers to receiver operating characteristics, which are chart plots used herein to measure the performance of a particular diagnostic method at various cutoff points. ROC plots may consist of true positives and false positives at various cutoff points.

[0079] As used herein, the term "AUC" refers to the area under the curve of a ROC plot. AUC can be used to estimate the predictive power of a particular diagnostic test. Generally, a larger AUC corresponds to increased predictive power and a decrease in the frequency of prediction errors. Possible AUC values ​​range from 0.5 to 1.0, with the latter being characteristic of error-free prediction methods.

[0080] As used herein, the terms “p-value” or “p” refer to the likelihood that the distributions of biomarker scores for lung cancer-positive and lung cancer-negative subjects are identical in the context of the Wilcoxon rank-sum test. Generally, a p-value close to zero indicates that a particular statistical method has high predictive power in classifying subjects.

[0081] As used herein, the term "CI" means a confidence interval, that is, an interval within which a particular value can be predicted to fall with a certain level of confidence. As used herein, the term "95% CI" means an interval within which a particular value can be predicted to fall with a 95% level of confidence.

[0082] As used herein, the term “positive predictive value” means the proportion of positive results induced by a particular method that are truly positive.

[0083] As used herein, the terms “disease progression” or “early disease progression” are defined as an upgrade in the Gleason score and / or an increase in tumor volume on a surveillance biopsy within 18 months of the commencement of active surveillance.

[0084] The phrase "therapeutically effective" is intended to quantify the amount of active ingredient used in the treatment of a disease or disorder, or the effect of a clinically evaluated measure.

[0085] List of abbreviations 4MP = 4-marker protein panel (pro-surfactant protein B (pro-SFTPB), mucin 16 (CA125), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1)); AUC = area under the curve; NSCLC = non-small cell lung carcinoma; SCLC = small cell lung carcinoma; PPV = positive predictive value; ROC = receiver operating characteristic. [Examples]

[0086] The following embodiments are included to demonstrate embodiments of the present disclosure. The following embodiments are provided merely as examples and to assist those skilled in the art using the present disclosure. The embodiments are not intended in any way to limit the scope of the present disclosure. Those skilled in the art will recognize that many modifications can be made in the particular embodiments disclosed and should obtain equivalent or similar results without departing from the spirit and scope of the present disclosure.

[0087] Example 1: Mortality benefit of a blood-based biomarker panel for lung cancer based on the PLCO cohort. To identify individuals at high risk of fatal lung cancer, we conducted a study to investigate the usefulness of integrating a panel of circulating protein biomarkers with a risk model based on subject characteristics. As further described below, PLCO was assayed in prediagnostic serum from 552 lung cancer cases and 2,193 non-cases from the prostate, lung, colorectal, and ovarian (PLCO) cohort. m2012Data from established logistic regression analysis models, combining a risk model with a 4-marker protein panel (4MP), were used in the study. Of 552 lung cancer cases, 387 (70%) died from lung cancer. The cumulative incidence of lung cancer deaths, as well as subdistributions and cause-specific hazard ratios, were analyzed using 4MP+PLCO at defined 6-year risk thresholds of 1.0% and 1.7%, corresponding to the previous and current US Preventive Services Task Force screening criteria. m2012 It was calculated based on the risk score.

[0088] When considering cases diagnosed within one year of blood collection and all non-cases, 4MP+PLCO is used to predict the risk of lung cancer death. m2012 The model's AUC estimate was 0.88 (95% CI: 0.86-0.90). The cumulative incidence of lung cancer deaths was 4MP+PLCO, exceeding the 6-year risk threshold of 1.0%. m2012 The score was statistically significantly higher in individuals with a score (adjusted χ²: 166.27, P<0.0001). The corresponding subdistribution and lung cancer death-specific hazard ratios for test-positive cases were 9.88 (95% CI: 6.44–15.18) and 10.65 (95% CI: 6.93–16.37), respectively.

[0089] PLCO cohort The PLCO cancer screening trial was a multicenter, randomized trial in the United States aimed to evaluate the impact of early detection procedures for prostate, lung, colorectal, and ovarian cancers on disease-specific mortality. A biorepository of blood samples collected annually from consenting intervention group participants was created. Cancer status was reported based on annual questionnaires. Medical records were obtained to examine the diagnostic course and characteristics of diagnosed lung cancers. TNM stages and staging groups were determined according to the 5th edition of the American Joint Committee on Cancer's Cancer Staging Manual. Treatment data were extracted from medical records for the first year after diagnosis. PLCO participants were followed for an additional 13 years after the completion of the PLCO study for lung cancer incidence and for 20 years for lung cancer mortality.

[0090] All deaths occurring during the trial were primarily identified through the annual study update questionnaire. Participants who did not return the questionnaire were contacted repeatedly by letter or telephone. To enhance the completeness of endpoint validation, active follow-up involved periodic association with the National Death Index. Death certificates were obtained to confirm death and determine a provisional cause of death. Because the underlying cause of death was not always accurately recorded on death certificates, the PLCO trial used an endpoint determination process to assign causes of death in a uniform and unbiased manner. All deaths with a potentially cancer-related cause were investigated by a death review committee consisting of a non-voting chair and three experienced investigators. Death investigators were blinded to the trial group of deceased participants. Lung cancer-specific deaths were defined as deaths with lung cancer as the underlying cause or with treatment for lung cancer.

[0091] Risk models based on the characteristics of the subject PLCO m2012This is a survey-based logistic regression analysis model that predicts the 6-year risk of lung cancer diagnosis. This period was selected to optimize the application and trial in the National Lung Screening Trial (NLST) and had a median follow-up of 6 years. PLCO m2012 The model's predictor variables are derived from baseline questionnaire information and include age, race / ethnic group, education level, body mass index, chronic obstructive pulmonary disease, personal history of cancer, family history of lung cancer, and smoking status (current vs. past), intensity, duration, and duration of abstinence. m2012 Details of the model and its implementation are described in Tammegi et al., 2013, NEJM 368:728-736, which are incorporated herein by reference in their entirety.

[0092] 4MP readings in PLCO sample set The sample set consisted of serum collected prior to lung cancer diagnosis from 552 cases and 2,193 non-case PLCO participants who had not been diagnosed with lung cancer during the study or the 13-year follow-up period. 4MP biomarker scores were calculated based on a logistic regression analysis model. PLCO was used as two different predictors in the logistic regression analysis. m2012 4MP+PLCO predicts lung cancer within one year by fitting the 4MP score and a linear predictor. m2012 A combinatorial model was developed.

[0093] statistical analysis 4MP score, PLCO m2012 Score, and 4MP+PLCO m2012Predefined weights and cutpoints were applied based on 1.0% and 1.7% 6-year risk thresholds for the scores. 6-year risk thresholds ≥1.0% and ≥1.7% were used, respectively, as these have been shown to yield a similar number of screening-eligible individuals, as with the USPSTF2021 and USPSTF2013 screening criteria. Assuming a limited number of cases with a smoking history <10 PY in the study sample set, the analysis focused on participants with ≥10 PY and was stratified into low, medium, and high-risk groups defined by pack years and years since quitting smoking (Tables 1-4).

[0094] [Table 1]

[0095] [Table 2]

[0096] [Table 3]

[0097] [Table 4]

[0098] 4MP score (1.8206) * Layer-specific cutpoints for 4MP (low, medium, and high risk) were estimated. At a 6-year risk threshold of 1.0%, 4MP scores above 13.579, 12.529, and 12.332 in the low, medium, and high risk layers were considered "test positive." At a 6-year risk threshold of 1.7%, 4MP scores above 14.117, 13.066, and 12.870 in the low, medium, and high risk layers were considered "test positive." Combined 4MP + PLCO m2012 Regarding this, at the 6-year risk thresholds of 1.0% and 1.7%, the scores exceeding -4.595 and -4.057 respectively (-11.836 + 1.6160) *4MP+0.9861 * (PLC O m2012 The score was considered "test positive". PLCO m2012 Regarding the score, PLCO m2012 The logit form of the risk model was used.

[0099] For AUC calculation, samples from all cases diagnosed within one year of blood collection and all non-case samples were considered. The event-positive group was defined as individuals who died due to lung cancer, while the event-negative group consisted of participants who did not die due to lung cancer (including censored information and other causes of death). The corresponding 95% confidence intervals for statistical parameters were estimated using 1,000 bootstraps.

[0100] In line with previous studies, survival analyses were performed among individuals with a smoking history of ≥10 pack-years (PY). In the PLCO dataset, deaths from causes other than lung cancer eliminate the incidence of lung cancer-specific deaths. In other words, individuals who die from other non-lung cancer-related causes no longer have a risk of lung cancer death. Therefore, other causes of death were considered competing risk events. To estimate the incidence of lung cancer death over time in the presence of competing risks, two different modeling approaches were used: cause-specific hazards for lung cancer death (where non-lung cancer deaths are treated as censored events) and sub-distribution hazards of the cumulative incidence function for lung cancer death.

[0101] In a cause-specific hazard function, k th The instantaneous hazard function for an event (where k means lung cancer death or non-lung cancer death) is:

number

[0102] For the hazard ratios of the lower distribution, the following modeling approach was followed: The hazard function of the lower distribution is k for objects that have not yet experienced an event of type k. thThe focus is on the risk of failure from an event (where k means lung cancer death or non-lung cancer death). This is:

number

[0103] Time to event was defined as the interval between blood samplings until death from lung cancer, death from another cause, or the final period of follow-up. Two curves in the cumulative incidence plot were compared using Gray's modified chi-squared test statistic.

[0104] All analyses were performed in R (version 4.2.0), using the "pROC" package for calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, and the "cmprisk" package for time-dependent survival analysis.

[0105] Lung cancer-specific mortality, 4MP+PLCO m2012 Predictive performance of combinatorial models Of the 552 lung cancer cases diagnosed during the 6-year PLCO study period, 387 (70%) died from lung cancer, 99 (18%) died from other causes, 41 (7%) were still alive at the final follow-up, and survival information was unavailable for 25 (5%) (Tables 1 and 5). Of the 2,193 non-cancerous participants, 556 (25%) died from other causes (Table 2). Notably, 8 individuals (0.004%) died from lung cancer after 13 years of follow-up regarding lung cancer development. These 8 individuals were excluded from subsequent analyses.

[0106] [Table 5]

[0107] The median survival time for lung cancer patients who died from lung cancer and were diagnosed within one year of blood sampling was 2.77 years (interquartile range (IQR): 2.60–3.02 years) (Table 4).

[0108] When examining serum collected within one year prior to lung cancer diagnosis and serum from all non-case cases, 4MP+PLCO m2012 The combined model had an AUC of 0.88 (95% CI: 0.86–0.90) for predicting the risk of lung cancer-specific mortality (Figures 1–5, Table 6). Similar performance estimations were found when examining unique case and non-case serum samples from randomly selected samples (Figure 6 and Table 7). 4MP+PLCO for lung cancer-specific mortality from non-small cell lung cancer (NSCLC) or small cell lung cancer (SCLC) diagnosis. m2012 The estimated performance of the combined models was 0.87 (95% CI: 0.85~0.89) and 0.86 (95% CI: 0.82~0.90), respectively. Notably, when stratified between individuals with and without chronic obstructive pulmonary disease (COPD), the 4MP+PLCO m2012 The models yielded AUC values ​​of 0.76 (95% CI: 0.69–0.84) and 0.88 (95% CI: 0.86–0.90), respectively, for predicting death due to lung cancer (Tables 6 and 7).

[0109] [Table 6]

[0110] [Table 7]

[0111] [Table 8]

[0112] [Table 9]

[0113] [Table 10]

[0114] [Table 11]

[0115] 4MP+PLCO against USPSTF criteria to predict lung cancer-specific mortality in smoking history ≥ 10 PY m2012 Model Comparison Following the USPSTF2013 and USPSTF2021 criteria for predicting lung cancer-specific mortality, the 4MP+PLCO m2012 The sensitivity and specificity of the combined models were compared. Compared with the USPSTF2013 criteria corresponding to a 6-year risk threshold ≥ 1.7%, the 4MP+PLCO m2012 The combined model showed improved sensitivity (85.0 (95% CI: 81.8~90.7)) compared to 74.0 (95% CI: 68.0~79.0) for predicting lung cancer death, specificity (71.0 (95% CI: 70.1~72.2) compared to 58.0 (95% CI: 57.0~59.0), and positive predictive value (PPV) (24.2% (95% CI: 22.8~25.1) compared to 16.3% (95% CI: 15.1~17.9). At a 6-year risk threshold ≥1.0% corresponding to the USPSTF2021 criteria, the 4MP+PLCO m2012 The combined model showed improved sensitivity of 90.2% (95% CI: 87.1-94.2%) compared to 81.0 (95% CI: 75.7-85.0), specificity of 58.1 (95% CI: 56.0-59.1) compared to 52.0 (95% CI: 50.0-53.0), and PPV of 19.3% (95% CI: 18.1-20.4) compared to 16.0% (95% CI: 13.9-17.4) (Tables 8-10).

[0116] [Table 12]

[0117] [Table 13]

[0118] [Table 14]

[0119] The incidence of lung cancer death among individuals with a smoking history of ≥10 years, and 4MP+PLCO at 6-year risk thresholds of 1.7% and 1.0%. m2012 Relationship Further lung cancer-specific survival analyses were conducted. These analyses examined all cases in which the sample was diagnosed within one year of blood collection. All non-symptom individuals with a smoking history of ≥10 PY were evaluated based on their 6-year risk thresholds of 1.7%, ≥1.0%, or <4MP+PLCO, respectively. m2012 Based on the model score, the results were divided into two categories: positive or negative.

[0120] Compared to PLCO individuals who tested negative (n=1,253), the cumulative incidence of lung cancer death was statistically significantly higher in test-positive cases (n=805) when considering a 6-year risk threshold of 1.7% (modified chi-squared: 277.04, P<0.0001). The subdistribution and lung cancer death-specific hazard ratios were 12.82 (95% CI: 8.67–18.77) and 17.08 (95% CI: 9.61–10.64), respectively. Compared to cases with negative test results (n=990), cases with positive test results (n=1,068) had a statistically significantly higher cumulative incidence of lung cancer death at a 6-year risk threshold of 1.0% (modified chi-squared: 166.27, P: <0.001). The subdistribution and lung cancer death-specific hazard ratios were 9.88 (95% CI: 6.44~15.18) and 10.65 (95% CI: 6.93~16.37), respectively (Figures 7-17; Tables 11-14).

[0121] [Table 15]

[0122] [Table 16]

[0123] [Table 17]

[0124] [Table 18]

[0125] [Table 19]

[0126] 4MP+PLCO m2012 Compared to the USPSFT criteria, it better predicts lung cancer-specific mortality, with improved sensitivity, specificity, and PPV. In the PLCO cohort, the LCDRAT model had a reported AUC of 0.81 (95% CI: 0.79-0.83) for predicting lung cancer death among individuals with a history of smoking. For comparison, 4MP+PLCO m2012 The model yielded an AUC of 0.88 (95% CI: 0.86–0.90) that predicts lung cancer-specific mortality among individuals with a history of smoking.

[0127] The 4MP trial will be expanded to include individuals who are currently eligible for LDCT screening and who have a smoking history of ≥10 PY. This corresponds to the USPSF2021 criteria: 4MP + PLCO for individuals with a 6-year risk of ≥1.0%. m2012 Based on the score, individuals identified as being at high risk of lung cancer incidence or death are examined in LDCT by collaborative decision-making. Even when eligible, enrollment in lung cancer screening programs remains resolutely below 15%, and positive biomarker testing can serve as a further driving force for individuals who are eligible to be screened. PLCO m2012For individuals lacking sufficient information to warrant a decision, 4MP alone may be used to inform them of the need for LDCT based on their individual risk profile. 4MP trials should be conducted regularly at trial intervals commensurate with the level of risk. For non-US countries that have not yet adopted the USPSFT2021 criteria or have not conducted an LCS, 4MP + PLCO with a stricter decision threshold of 1.7% or higher at 6 years risk may be used. m2012 The improved performance allows for the selection of individuals at very high risk of lung cancer death who would likely benefit from LDCT, while simultaneously limiting the number of false positives associated with a low risk threshold.

[0128] PLCO m2012 A blood-based 4MP biomarker panel used in conjunction with the model provides an improved means for individualized risk assessment of fatal lung cancer compared to current USPSTF criteria, identifying individuals at high risk of fatal lung cancer. This trial has the potential to better select individuals who would benefit from DCT screening.

[0129] All references, U.S. or foreign patents or patent applications cited herein are incorporated herein in their entirety as if they were contained herein. In the event of any discrepancy, the material disclosed herein literally shall be in control.

[0130] From the above description, those skilled in the art will readily understand the essential features of the present invention and can make various changes and modifications to it to adapt it to various uses and conditions without departing from its spirit and scope.

Claims

1. A method for predicting the prognosis of an individual with lung cancer: PLCO ｍ２０１２ A step in which a combined model score is determined using logistic regression analysis together with a risk model score and a biomarker score, the steps comprising measuring the levels of biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample taken from a patient based on the biomarker score; A method in which the aforementioned combined model score indicates the prognosis of the individual with respect to progression-free survival and overall survival.

2. The method according to claim 1, wherein if the combined model score exceeds a defined risk threshold, the individual is predicted to have a poor survival prognosis for lung cancer.

3. A method for predicting disease outcomes in individuals with lung cancer: PLCO ｍ２０１２ A step of determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, wherein the levels of the biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample taken from a patient are measured from the biomarker score; and A step in which individuals with an increased risk of adverse disease outcomes are identified when the aforementioned combined model score exceeds a defined risk threshold; A method that includes this.

4. A method for identifying individuals at high risk of death from lung cancer: PLCO ｍ２０１２ A step of determining a combined model score using logistic regression analysis together with a risk model score and a biomarker score, wherein the levels of the biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample taken from a patient are measured from the biomarker score; and A step in which individuals at high risk of death from lung cancer are identified if the aforementioned combined model score exceeds a defined risk threshold; A method that includes this.

5. The aforementioned PLCO ｍ２０１２ The method according to any one of claims 1 to 4, wherein the risk model score is based on baseline questionnaire information including age, race or ethnic group, education level, body mass index, chronic obstructive pulmonary disease, personal history of cancer, family history of lung cancer, and smoking status (current vs. past), intensity, duration, and duration of abstinence.

6. The combined model score is given by the equation: -11.836 + 1.6160 ＊ (0.4730 ＊ log[CA125] + 0.6531 ＊ log[CEA] + 0.2612 ＊ log[CYFRA21-1] + 0.9238 ＊ log[Pro-SFTPB]) + 0.9861 ＊ (PLCO ｍ２０１２ score), the method according to any one of claims 1 to 5.

7. The method according to any one of claims 1 to 6, wherein the defined risk threshold is a 6-year risk threshold of 1.0%.

8. The method according to claim 7, wherein a combination model score greater than -4.595 is considered to be positive for the test.

9. The method according to any one of claims 1 to 6, wherein the defined risk threshold is a 6-year risk threshold of 1.7%.

10. The method according to claim 9, wherein a combination model score greater than -4.057 is considered to be positive for the test.

11. The method according to any one of claims 1 to 10, wherein the individual is asymptomatic.

12. The method according to any one of claims 1 to 11, wherein the aforementioned individual is at high risk.

13. The method according to any one of claims 1 to 12, further comprising calculating the lifespan of the individual.

14. The method according to any one of claims 1 to 13, wherein the levels of the biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample obtained from the individual are determined by immunoassay.

15. The method according to any one of claims 1 to 14, wherein each of the biomarkers CEA, CA125, CYFRA21-1, and Pro-SFTPB in a biological sample obtained from the individual generates a detectable signal.

16. The method according to claim 15, wherein the detectable signal is detectable by spectroscopy.

17. The method according to claim 16, wherein the spectroscopic method is selected from ultraviolet-visible spectroscopy, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, nuclear magnetic resonance (NMR) spectroscopy measurement, gas chromatography, mass spectrometry (GC-MS), liquid chromatography-mass spectroscopy (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating coordinate system nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC-TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectroscopy.

18. The method according to claim 17, wherein the spectroscopic method is a mass spectrometry method.

19. The method according to claim 18, wherein the mass spectrometry method is LC-TOF-MS.

20. The method according to any one of claims 1 to 19, wherein the lung cancer is in an early stage (for example, stage I or II).

21. The method according to any one of claims 1 to 20, wherein the lung cancer is in an advanced stage (for example, stage III or IV).

22. The method according to any one of claims 1 to 21, wherein the individual has a smoking history of 10 pack years or more.

23. The method according to any one of claims 1 to 22, wherein the individual is between 50 and 80 years of age.

24. The method according to any one of claims 1 to 23, wherein the AUC of the method is greater than the AUC of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

25. The method according to claim 24, wherein the AUC is greater than 0.

85.

26. The method according to claim 25, wherein the AUC is 0.86 to 0.

90.

27. The method according to claim 26, wherein the AUC is approximately 0.

88.

28. The method according to any one of claims 1 to 23, wherein the sensitivity and specificity values ​​of the method at a 6-year risk threshold of 1.7% or higher are greater than the sensitivity and specificity values ​​of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

29. The method according to claim 28, wherein the sensitivity exceeds 0.80 and the specificity exceeds 0.

65.

30. The method according to claim 29, wherein the sensitivity is 0.82 to 0.91 and the specificity is 0.70 to 0.

72.

31. The method according to claim 30, wherein the sensitivity is approximately 0.85 and the specificity is approximately 0.

71.

32. The method according to any one of claims 1 to 23, wherein the sensitivity and specificity values ​​of the method at a 6-year risk threshold of 1.0% or higher are greater than the sensitivity and specificity values ​​of different biomarkers, biomarkers, panels, assays, algorithms, models, or any combination thereof.

33. The method according to claim 32, wherein the sensitivity exceeds 0.85 and the specificity exceeds 0.

55.

34. The method according to claim 33, wherein the sensitivity is 0.87 to 0.94 and the specificity is 0.56 to 0.

59.

35. The method according to claim 34, wherein the sensitivity is approximately 0.90 and the specificity is approximately 0.

58.

36. The method according to any one of claims 1 to 35, wherein the individual is subsequently designated for further lung cancer screening or treatment.

37. The method according to claim 36, wherein the screening is selected from endoscopic ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT) scans.

38. The method according to claim 37, wherein the screening is performed annually.

39. The method according to claim 37, wherein the screening is performed every six months.

40. The method according to claim 36, wherein the treatment is selected from surgery, chemotherapy, immunotherapy, radiotherapy, targeted therapy, or a combination thereof.

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