Systems and methods for determining lung health

Abstract

A method for predicting the likelihood that a subject has a lung disease comprises labeling an ex vivo sputum sample from the subject with one or more of the following probes: a first labeled probe that binds to a biomarker expressed on a white blood cell population in the sample; a second labeled probe selected from the group consisting of a granulocyte probe, a T cell probe, a B cell probe, or any combination thereof; a third labeled probe that binds to a biomarker on a macrophage population; a fourth labeled probe that binds to disease-associated cells in the sample; a fifth labeled probe that binds to a biomarker expressed on an epithelial cell population; and a sixth labeled probe that binds to a cell surface biomarker expressed on the epithelial cell population, to obtain data comprising a mean fluorescence characteristic and detecting a distribution based on the presence or absence of the labeled probes.

Description

[0001] References to Related Applications

[0002] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 657,584, filed April 13, 2018, entitled “Systems and Methods for Determining Lung Health,” the specification and claims of which are incorporated herein by reference.

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] not applicable.

[0005] Names of the parties to the joint research agreement

[0006] not applicable.

[0007] Incorporation by reference of material submitted on CD-ROM

[0008] not applicable.

[0009] Statement regarding prior disclosures by the inventor or co-inventor

[0010] not applicable.

[0011] Copyrighted Materials

[0012] not applicable. Background Art

[0013] Note that the following discussion refers to many documents by author and publication year, and some documents are not considered prior art with respect to the present invention due to their most recent publication date. The discussion of these documents in this article is intended to provide a more complete background and should not be interpreted as an admission that these documents are prior art for patentability determination purposes.

[0014] Low-dose computed tomography (LDCT) is the current standard of care for lung cancer screening as an early diagnostic method, particularly in the high-risk population defined by the Centers for Medicare and Medicaid Services (CMS) in the United States: individuals aged 55 to 75 years who have smoked the equivalent of a pack of cigarettes per day for 30 years and have not quit in the past 15 years. However, according to the National Lung Cancer Screening Trial (LCST), the largest lung cancer screening trial to date, LDCT had a sensitivity of 93.8% and a specificity of 73.4%. The LCST demonstrated a 3.8% false-positive LDCT rate in its high-risk population, leading to numerous unnecessary, often invasive, and potentially harmful follow-up procedures for patients who tested positive on LDCT but did not have lung cancer. Therefore, there is a pressing need to improve the specificity of LDCT and thereby reduce its false-positive rate. One approach to addressing this need is to develop additional tests with high specificity for lung cancer that can serve as an adjunct to LDCT. Highly fluorescent tetrakis(4-carboxyphenyl)porphyrin (TCPP) selectively binds to cancer cells compared to normal cells, making it particularly suitable for developing diagnostic markers that can distinguish cancer cells from surrounding background cells. The standard of care for screening individuals at high risk for lung cancer includes annual imaging of the chest using LDCT (1). Although LDCT is extremely sensitive, it has a high false-positive rate, subjecting patients who ultimately test negative for cancer to multiple reflex diagnostic procedures and their associated risks. These risks include exposure to additional high-dose radiation and complications and morbidity from invasive procedures such as thoracentesis, bronchoscopy, and core needle biopsy. The risk of adverse events and the additional economic burden associated with these procedures are significant, creating a clear medical need for safer and less invasive reflex testing after a positive LDCT result (2). Alternative test methods would ideally complement the high sensitivity of LDCT by increasing specificity, reducing false-positive rates, and improving the positive predictive value of screening with reasonably priced adjunctive tests.

[0015] Minimally invasive techniques in the form of liquid biopsies have been proposed for reflex lung cancer detection following a positive LDCT result. Using liquid biopsies, circulating tumor cells (CTCs) and free tumor nucleic acids are collected from a patient's peripheral blood sample. The CTCs and nucleic acids are tested using molecular techniques such as next-generation sequencing (NGS) to determine the presence of cancer-associated gene mutations that may predict the presence of cancer and how the patient's tumor will respond to specific targeted therapies (3). Although these techniques can identify mutations in approximately 50-75% of lung cancers (4,5), patients with a positive LDCT whose tumors do not have this specific genetic abnormality will have a negative liquid biopsy result. In addition, CTCs are rare (as low as 10 per 10). 9Liquid biopsies have the potential to provide valuable therapeutic information about a patient’s tumor genome, but are better used later in the lung cancer diagnostic algorithm than in tests aimed at early-stage cancer diagnosis.

[0016] Liquid-based cytology of bronchial washings provides a method for sampling potentially malignant cells for pathological examination using conventional sputum smears. The bronchoscopic procedure used to remove cells from a patient's airways is less invasive than a core needle lung biopsy. However, there is still a risk of adverse events such as bleeding (7). In addition, the associated health care costs, especially when performed on an inpatient basis, can be high. Given that only a small proportion (i.e., less than 4%) of patients with positive LDCT will actually be found to have lung cancer, there remains a medical need for an economical and more accessible alternative source of malignant lung cells to provide diagnostic material.

[0017] For decades, pathologists have performed routine cytology of sputum as a non-invasive, rapid, and specific method for detecting lung cancer. In routine sputum cytology, the sample is stained and screened for malignant cells under a microscope. However, the sensitivity of routine sputum cytology is low (approximately 65%) (8). Various approaches have been tried to improve the sensitivity of sputum analysis, including KRAS mutation testing. Although KRAS testing is both sensitive and specific when the patient's tumor actually has a KRAS mutation, only 15-20% of lung cancers actually have a KRAS gene mutation. Therefore, tumor cells that are negative for KRAS mutations will not be detected by this technology (9). Another DNA-based method, called automated sputum cytometry, uses special stains and computer-assisted image analysis to evaluate the nuclear DNA characteristics of sputum epithelial cells to detect changes associated with malignancy. Although this technology is more sensitive than routine cytology, its specificity is only about 50% (10). Summary of the Invention

[0018] One embodiment of the present invention provides a method for predicting the likelihood of a subject having a lung disease, the method comprising the steps of: labeling an ex vivo sputum sample with one or more of the following probes: i) a first labeled probe that binds to a biomarker expressed on a leukocyte population of sputum cells; ii) a second labeled probe selected from the group consisting of a granulocyte probe that binds to a biomarker expressed on a granulocyte population of sputum cells, a T cell probe that binds to a biomarker expressed on a T cell population of sputum cells, a B cell probe that binds to a biomarker expressed on a B cell population of sputum cells, or any combination thereof; iii) a third labeled probe that binds to a biomarker on a macrophage population; iv) a fourth labeled probe that binds to a disease-associated cell in the sputum sample; v) a fifth labeled probe that binds to a biomarker expressed on an epithelial population of sputum cells; and vi) a sixth labeled probe that binds to a cell surface biomarker expressed on an epithelial population of sputum cells. Analyzing the labeled sputum sample (e.g., by flow cytometry) to obtain data comprising cell count data per cell based on the mean fluorescence characteristic of any one of the labeled probes i)-vi). The per-cell data is detected to determine the likelihood that the subject suffers from a lung disease based on a distribution diagram of the presence or absence of labeled probes in the per-cell labeled data. The obtained data can be further analyzed to determine whether a biomarker is present in the sputum sample. For example, the disease-associated cells can be lung cancer cells or tumor-associated immune cells. The lung disease can be one selected from the group consisting of asthma, chronic obstructive pulmonary disease (COPD), influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer. In addition, the labeled sputum cells can be fixed or non-fixed.

[0019] Data collected from labeled sputum samples can be characterized by cell populations and biomarkers identified therefrom. For example, the ratio of sputum cells negative for i) to sputum cells positive for i) can be determined in the data collected from the labeled sputum sample to identify biomarker 1. In one example, a ratio of less than 2 indicates that the sputum sample is positive for biomarker 1. In one embodiment, a positive biomarker 1 has a sensitivity of at least approximately 80% and a specificity of at least 50% for distinguishing between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarker 1. The sensitivity is at least 85%, 90%, or 95%, and the specificity is at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0020] In another example, sputum cells that are negative for i) and positive for iv) and v) are identified based on data collected from a labeled sputum sample to identify biomarker 2. For example, a percentage of sputum cells that are negative for i) and positive for iv) and v) greater than 0.03% indicates that the sputum sample is positive for biomarker 2. In one embodiment, a positive biomarker 2 has a sensitivity of at least 90% and a specificity of at least 50% for distinguishing between lung cancer (c) sputum samples and high risk (HR) sputum samples using biomarker 2. The sensitivity is at least 80%, 85%, or 95%, and the specificity is at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0021] In another example, biomarker 3 is identified if sputum cells are positive for i) and iii) and exhibit FITC autofluorescence. For example, a percentage of sputum cells that are positive for i) and iii) and exhibit FITC autofluorescence greater than 0.03% indicates that the sputum sample is positive for biomarker 3. In one embodiment, a positive biomarker 3 has a sensitivity of at least 60% and a specificity of at least 70% for distinguishing between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarker 3. The sensitivity is at least 65%, 70%, 75%, 80%, 85%, 90%, or 95%, and the specificity is at least 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0022] In another example, if sputum cells are negative for i) and positive for v) and vi) for identifying biomarker 4, then biomarker 4 is identified. For example, if the percentage of cells negative for i) and positive for v) and vi) exceeds 2%, it indicates that the sample is positive for biomarker 4. In one embodiment, positive biomarker 4 has a sensitivity of at least 70% and a specificity of at least 70% for distinguishing between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarker 3. The sensitivity is at least 80%, 85%, 90%, or 95%, and the specificity is at least 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0023] In another embodiment, more than one biomarker can be used in combination (e.g., positive biomarker 1 combined with positive biomarker 2) to produce a sensitivity of at least 80% and a specificity of at least 80% for differentiating between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarkers 1 and 2. Furthermore, a combination of positive biomarkers 1, 2, and 3 can produce a sensitivity of at least 80% and a specificity of at least 80% for differentiating between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarkers 1-3. Furthermore, positive biomarkers 1-4 can produce a sensitivity of at least 70% and a specificity of at least 75% for differentiating between lung cancer (c) sputum samples and high-risk (HR) sputum samples using biomarkers 1-4. The sensitivity is at least 70%, 75%, 80%, 85%, 90%, or 95%, and the specificity is at least 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0024] In one embodiment, the flow cytometric analysis can include one or more of the following steps: excluding from data analysis cells with a diameter less than about 5 microns and greater than about 30 microns, dead cells, and cell clusters consisting of more than one cell.

[0025] In another embodiment, the first label probe that binds to a biomarker expressed on a leukocyte population of sputum cells can be a CD45 antibody or a fragment thereof.

[0026] In another embodiment, the second marker probe is one or more of the following probes added to the sputum sample, independently or in combination: a granulocyte probe that binds to a biomarker expressed on the granulocyte population of sputum cells, which granulocyte probe can be selected from CD66b antibody or a fragment thereof; a T cell probe that binds to a biomarker expressed on the T cell population of sputum cells, which T cell probe is a CD3 antibody or a fragment thereof; a B cell probe that binds to a biomarker expressed on the B cell population of sputum cells, which B cell probe is a CD19 antibody or a fragment thereof.

[0027] In another embodiment, the third marker probe that binds to a biomarker expressed on a macrophage population of sputum cells is a CD206 antibody or a fragment thereof.

[0028] In another embodiment, the fourth labeled probe that binds to disease-associated cells in a sputum sample is tetrakis(4-carboxyphenyl)porphyrin (TCPP).

[0029] In another embodiment, the fifth marker probe that binds to a biomarker expressed on the epithelial cell population of sputum cells is a pancytokeratin antibody or a fragment thereof.

[0030] In another embodiment, the sixth marker probe that binds to a cell surface biomarker expressed on the epithelial cell population of sputum cells is an epithelial cell adhesion molecule (EpCam) antibody or a fragment thereof.

[0031] The data collected may include cell counting data per cell based on the mean fluorescence characteristics of any one of i)-vi) labeled probes to produce sputum sample features. The sputum sample features identify the health status and / or lung disease of the lungs. The lung disease can be selected from the group consisting of asthma, chronic obstructive pulmonary disease (COPD), influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease and lung cancer. In addition, the sputum sample features are compared with a database of control sputum sample features (no disease) and lung disease sample features to identify lung disease. In some embodiments of the present invention, a trained algorithm is used to classify the results. The trained algorithm of the present invention includes a reference set of known sputum samples from a subject with a high risk of morbidity, a sputum sample confirmed as a sick subject, and an algorithm developed from a sputum sample of a subject identified as normal (no disease or high risk of morbidity). Algorithms suitable for sample classification include but are not limited to k nearest neighbor algorithms, concept vector algorithms, naive Bayesian algorithms, neural network algorithms, hidden Markov model algorithms, genetic algorithms, and mutual information feature selection algorithms or any combination thereof. In some cases, the trained algorithms of embodiments of the present invention may be combined with data other than sputum sample characteristics or cell count data per cell or mean fluorescence characteristics, such as diagnostic information given by a cytologist or pathologist or information about the subject's medical history. The data is input into the trained algorithm in a programmed computer to generate a classification of the sputum sample as having a high probability, a medium probability, or a low probability of having a lung disease, and electronically output a report confirming the classification of the sputum sample as having a lung disease.

[0032] One embodiment of the present invention provides a first reagent composition for performing flow cytometric phenotypic analysis on sputum cells in a sputum sample from a subject to identify one or more biomarkers in the cell population that are associated with the likelihood of lung disease, wherein the reagent composition comprises: i) tetrakis(4-carboxyphenyl)porphyrin (TCPP) fluorescent dye; and a fluorescent dye-conjugated antibody against a cell marker, wherein the cell marker is selected from: ii) epithelial cell adhesion molecule (EpCAM) and / or pancytokeratin, and iii) CD45, CD206, CD3, CD19, CD66b, or any combination thereof.

[0033] Another embodiment of the present invention provides a second reagent composition for performing flow cytometric phenotyping analysis on sputum cells in a sputum sample from a subject to identify one or more biomarkers in the cell population that are associated with the likelihood of lung disease, wherein the reagent composition comprises: i) tetrakis(4-carboxyphenyl)porphyrin (TCPP) fluorescent dye and fluorescent dye-conjugated antibodies against the following cell markers: ii) epithelial cell adhesion molecule (EpCAM) and / or pancytokeratin and iii) CD45.

[0034] Another embodiment of the present invention provides a third reagent composition for performing flow cytometric phenotyping analysis on sputum cells in a sputum sample from a subject to identify one or more biomarkers in the cell population that are associated with the likelihood of lung disease, wherein the reagent composition comprises: i) tetrakis(4-carboxyphenyl)porphyrin (TCPP) fluorescent dye; and fluorescent dye-conjugated antibodies against one or more of the following cell markers: CD45, CD206, CD3, CD19, and CD66b.

[0035] Another embodiment provides a method for predicting the possibility of a subject suffering from a lung disease, the method comprising the following steps: labeling an ex vivo sputum sample with i) a labeled probe that binds to disease-related cells in a sputum sample and ii) one or more fluorescent dye-binding probes labeled for sputum cells. The labeled sputum sample is subjected to flow cytometry analysis to obtain data, the data comprising cell count data per cell based on the mean fluorescence characteristics of any one of i)-ii) labeled probes. The possibility of a subject suffering from a lung disease is detected from the per cell data based on the presence or absence of a distribution diagram of i) and ii) in the per cell labeled data. The data comprising the cell count data per cell can be based on the mean fluorescence characteristics of any one of i)-ii) to produce a sputum sample feature. In one embodiment, the sputum sample feature identifies a lung disease, for example, the lung disease is selected from the group consisting of asthma, chronic obstructive pulmonary disease, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer. In addition, the sputum sample feature is compared with a database of control sputum sample features (no disease) and lung disease sample features to identify lung disease from the labeled sputum sample. In one embodiment, the labeled probe that binds to disease-associated cells in a sputum sample is tetrakis(4-carboxyphenyl)porphyrin (TCPP).

[0036] Further scope of applicability of the present invention will be partially set forth in the detailed description below in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art upon study of the following or may be learned by practice of the present invention. The objects and advantages of the present invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one or more embodiments of the present invention and, together with the description, are used to explain the principles of the present invention. The accompanying drawings are only for the purpose of illustrating one or more embodiments of the present invention and should not be interpreted as limiting the present invention. In the drawings:

[0038] Figure 1A -B shows a spin smear from dissociated sputum cells, which is a spin smear stained with Wright-Giemsa of processed sputum cells before staining with antibodies or TCPP;

[0039] Figure 1C -E shows a flow cytometry-based system having a light source and a detector for analyzing the optical properties of forward scatter (FSC) and side scatter (SSC) of cells or microparticles, which are identified as exemplary optical properties of cells or microparticles that change over time as they pass through the area of a laser light source, with measurements of pulse height and area at Figure 1D As shown in the histogram shown;

[0040] Figure 2A -I shows microbeads ( Figure 2A and Figure 2G ) and cells ( Figure 2B -F, Figure 2H and Figure 2I ) of flow cytometry analysis dot plots ( Figure 2A -F) and contour plots ( Figure 2G -I);

[0041] Figure 3A -K shows dot plots and contour plots for identifying and characterizing hematopoietic cells in sputum;

[0042] Figure 4A -G shows CD45 exposed to CD66b probe or CD206 probe 阳性 Dot plot of sputum cells ( Figure 4A 、 Figure 4C 、 Figure 4F -G) and histogram ( Figure 4B 、 Figure 4D and Figure 4E );

[0043] Figure 5 The number of macrophages on each slide is shown as solid circles with an "x" inside on the y-axis and CD45 is shown as solid circles. 阳性 / CD206 阳性 The number of cells is shown on the x-axis as the sample number, where CD206 阳性The presence of cell clusters is consistent with the presence of numerous macrophages on sputum smears;

[0044] Figure 6 Flowchart showing the preparation of sputum samples for analysis. TM Green labels HCC15 cancer cells (step 1), while in another tube, dissociated sputum cells are stained with PE-labeled anti-CD45 antibody (step 2);

[0045] Figure 7A -F shows a dot plot of sputum cells, where Figure 7A indicates the CD45 gate, Figure 7B CD45 阳性 TCPP gate in cells, Figure 7C CD45 阴性 TCPP gate in cells, Figure 7D -F indicates unstained sputum cells and stained sputum cells treated with isotype control;

[0046] Figure 8A -B shows a preliminary comparative analysis of sputum samples obtained from healthy volunteers and high-risk patients with and without lung cancer. Figure 6 Similar to the experiment detailed in Figure 7, five sputum samples from different donors were analyzed. Open dots represent samples from healthy volunteers (H), black dots represent samples from high-risk patients without cancer (HR), and dots with an x represent samples from patients diagnosed with lung cancer (C). Figure 8A The expression of CD45 in each sample analyzed is shown. 阴性 (Left) and CD45 阳性 Total number of cells (right). Figure 8B The expression of CD45 in each sample analyzed is shown. 阴性 (Left) and CD45 阳性 (Right) TCPP in cells 阳性 the proportion of cells;

[0047] Figures 9A-9F An embodiment of the present invention is shown for analyzing whether TCPP exists in sputum cells. 阳性 Dot plot of one strategy of cells;

[0048] Figure 10A -B shows the quality control beads and sputum sample tube #6 described in the protocol analyzed by flow cytometry and the resulting dot plot. Figure 10A Shown is a bead size exclusion ("BSE") gate (box) that was first set on a profile obtained from running quality control beads. Figure 10B The BSE gate applied to all sputum samples is shown;

[0049] Figure 11A -F shows a sputum sample analyzed by flow cytometry and the resulting dot plot used to determine the Figure 11A 、 Figure 11B Unstained sputum cells (tube #4) as shown and Figure 11C Sputum cells stained as shown (test tube #6) to identify Figure 11C The live cells (LC) shown in the box and Figure 11D Single cells (SC) are shown. Figure 11E and Figure 11F Shown is a dot plot of sputum cells used to set isotype controls Figure 11E and the remaining CD45 after applying BSE, LC, and SC gates 阳性 CD45 阴性 cell populations;

[0050] Figure 12A -C shows the CD45 of sputum sample from tube #6 阳性 Cell analysis. All distribution plots depict CD45 cells selected by BSE, LC, and SC gates. 阳性 cell;

[0051] Figure 13A -B shows dot plots of an isotype control of FITC / Alexa488 (F / A) (tube #5) and cells treated with a probe for CD66b / CD3 / CD19 cell labeling that binds to (F / A) (tube #6);

[0052] Figure 14A -B shows a dot plot of PE-CF594 isotype control (test tube #5) and cells treated with a CD206 cell marker probe that binds to PE-CF594;

[0053] Figure 15A -B shows a dot plot of sputum cells (tube #5) with an isotype control of FITC / Alexa488 on the y-axis and PE-CF594 on the x-axis. A double negative gate or population 1 parameter was established. Figure 15A For the use of BSE, LC and CD45 阳性 Dot plot of isotype control gated by cell gate, Figure 15B Pseudo-color plots. The horizontal dashed lines represent the FITC / Alexa488 positive / negative cutoff values determined in Figure 13, while the vertical dashed lines are derived from the PE-CF594 positive / negative cutoff values determined in Figure 14;

[0054] Figure 16A-B shows the dot plot (A) and pseudocolor plot (B) of the sputum sample from tube #6 and was measured for mean fluorescence intensity of a mixture of CD66b / CD3 / CD19-FITC / Alexa488 antibodies (y-axis) and the marker CD206 conjugated to PE-CF594 (x-axis). CD45 was also selected by BSE, LC, and SC gates. 阳性 Cells. As shown in Figure 15, the same population 1 (solid inner box) and cutoff point (dashed line) were used for these distribution plots;

[0055] Figure 17A -C shows sputum CD45 from two samples (same for A and B) 阳性 Pseudo-color plots were generated for the tubes and gates set for populations 2-6 of the sputum samples of Figure 16 were applied. All distribution plots show CD45 gated by BSE, LC, and SC gates. 阳性 Sputum cells. Horizontal and vertical dashed lines are set on isotype controls (not shown). Figure 17A -B shows the situation in the graph of gates 4 and 5 when the FITC mean fluorescence intensity of population 5 is in the middle and crosses the horizontal cutoff line. Figure 17C Group 6 is shown in the upper right box;

[0056] Figure 18 The graph shows on the y-axis all blood cells (CD45 阳性 ) percentage (%), and distribution types 1, 2, and 3 are shown on the x-axis. The features shown are for CD45 阳性 Distribution of cells in Figure 1;

[0057] Figure 19A Figure C shows CD45 from HR and cancer cells 阳性 Characteristics 1-3 of sputum cells and all CD45 阳性 The percentage of blood cells shows the analysis results of population 6;

[0058] Figure 20A -D shows CD45 阴性 Dot plot of sputum sample showing gates for different epithelial subpopulations in sputum;

[0059] Figure 21A -B shows FITC / Alexa488 and CD45 阴性 Dot plots of isotype control (tube #5) and panCytokeratin / Alexa488-labeled sputum cells (tube #7). The cutoff for positive FITC / Alexa488 staining in CD45 sputum cells was determined;

[0060] Figure 22A -B shows dot plots of PE-CF594 and isotype control of sputum cells (test tube #5) and sputum cells labeled with EpCAM-PE-CF594 (test tube #7). 阴性 cutoff values for positive PE-CF594 staining in sputum cells and sputum;

[0061] Figure 23A -B shows CD45 that have been gated by BSE, LC and CD45 cell gates. 阴性 Dot plots of cells and isotype control (test tube #5). The horizontal dashed line represents the FITC / Alexa488 positive / negative cutoff value determined in Figure 21, while the vertical dashed line is derived from the PE-CF594 positive / negative cutoff value determined in Figure 22;

[0062] Figure 24A -B shows CD45 阴性 Dot plots of sputum cells and gates for cell populations 2–9;

[0063] Figure 25 Individual CD45 chromatograms 1-4 are shown with different characteristics of populations 1-9. 阴性 Dot chart;

[0064] Figure 26 Features of distribution plot 1 across the midline of population 1, population 2, population 5, and panCK+ are shown;

[0065] Figure 27 CD45 expression in sputum samples from subjects classified as having a high risk of lung cancer and in sputum samples from subjects classified as having lung cancer is shown. 阴性 Comparison of cell features 1-4;

[0066] Figure 28A -B shows the total CD45 阴性 The amount of PanCK++ expressed as a percentage (%) of cells (populations 3+4+9) determined that 80% sensitivity and 85% specificity were achieved when the biomarker was applied;

[0067] Figure 29A -C shows the analysis of cancer risk of cells in sputum samples obtained from HR and C sputum samples to determine the CD45 阴性 / CD45 阳性 ratio (biomarker 1);

[0068] Figure 30A-B shows that applying biomarker 1 to the analyzed sputum samples achieved 90% specificity and 54% sensitivity in identifying samples from lung cancer patients or subjects at high risk of developing lung cancer;

[0069] Figure 31A -C shows CD45 in sputum sample (test tube #7) positively labeled with TCPP (biomarker 2). 阴性 Cancer risk analysis of cells;

[0070] Figure 32A -B shows that applying biomarker 2 to the analyzed sputum samples achieved 63% specificity and 100% sensitivity in identifying samples from lung cancer patients or subjects at high risk of developing lung cancer;

[0071] Figure 33A -C shows the use of Figure 25 and Figure 27 The combination of biomarker 1 and biomarker 2 identified in the present invention can achieve 90% sensitivity and 90% specificity when analyzing sputum samples obtained from HR and C sputum samples. In one embodiment of the present invention, samples from lung cancer patients or subjects with a high risk of developing lung cancer can be identified by applying biomarker 1+2 to the analyzed sputum samples.

[0072] Figure 34A -C shows CD45 阳性 Dot plots of cells to identify the percentage of cells in population 6 (biomarker 3) from HR and C sputum samples as a percentage of all CD45+ cells in the sample;

[0073] Figure 35A -B shows that applying biomarker 3 to the analyzed sputum samples achieved 88% specificity and 60% sensitivity in identifying samples from lung cancer patients or subjects at high risk of developing lung cancer;

[0074] Figure 36A -B shows CD45 in sputum samples 阴性 Cancer risk analysis of cells, the sputum samples were also panCytokeratin in populations 3+4 and 9 of HR and C sputum samples 阳性 (biomarker 4);

[0075] Figure 37A -B shows that applying biomarker 4 to the analyzed sputum samples achieved 83% specificity and 80% sensitivity in identifying samples from lung cancer patients or subjects at high risk of developing lung cancer;

[0076] Figure 38A-E shows that biomarkers 1-4 were applied to HR and C sputum samples to perform cancer risk analysis on cells from sputum samples with a specificity of 98% and a sensitivity of 78%;

[0077] Figure 39 A flow chart for screening a subject's lung health status is shown, the screening flow chart including the systems and methods described herein for isolating cell populations from the lungs, and algorithms for classifying sputum samples as high, intermediate, and low risk for lung disease. DETAILED DESCRIPTION

[0078] In addition, the following terms shall have the following definitions. It should be understood that for any particular term not defined below, the term shall have the meaning typically used by persons of ordinary skill in the art in the context.

[0079] It must be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0080] The term "calibration" refers to setting the sensitivity of the machine based on control reagents.

[0081] The term "compensation" refers to comparing a sample to a control to determine background.

[0082] The term "separation" or "isolated" refers to selecting a subset of events for further analysis. An example of separation is the use of a "gate" to exclude / include data during analysis.

[0083] The term "gating" refers to setting boundaries around populations of cells that share common characteristics (usually forward scatter, side scatter, and marker expression) to investigate and quantify these populations of interest.

[0084] The term "probe" refers to a ligand, peptide, antibody, or fragment thereof that has affinity for and binds to a biomarker on the surface of a cell or microparticle or a marker within a cell or microparticle.

[0085] Porphyrin accumulation is observed in all types of cancer cells. In addition, some porphyrins are naturally fluorescent, with characteristic photon emission spectra. Herein, a porphyrin composition is described for use in high-throughput analysis, particularly flow cytometry, to distinguish the fluorescence of porphyrins that label cancer cells or cells associated with a disease state from surrounding background cells (11).

[0086] Now please refer to Figure 1A -B, which shows a centrifugation smear obtained from dissociated sputum cells. Figure 1A Wright-Giemsa-stained spin smears of treated sputum cells before staining with antibodies or TCPP are shown in FIG. Figure 1AContains an excess of buccal epithelial cells (BECs) (some of which are indicated by * symbols). Macrophages are indicated by arrows, and debris is indicated by arrowheads. Figure 1B There is less debris in the image (indicated by arrows) to allow identification of cheek epithelial cells and macrophages on the slide.

[0087] In flow cytometry, each cell or particle is hydrodynamically focused onto a phototube. As the cell / particle passes through the phototube, it passes through one or more beams of light. Light scatter or fluorescence (FL) emission (if the cell or particle is labeled with a fluorophore) provides information about the cell / particle's properties. Lasers are the most commonly used light sources in modern flow cytometry. Lasers produce a single wavelength of light (the laser line) at discrete frequencies (coherent light). They are available in wavelengths ranging from the ultraviolet to the far infrared and with a variable range of power levels (photon output / time). Light scattered forward (typically 20° off the axis of the laser beam) is collected by a photomultiplier tube (PMT) or photodiode in a channel called forward scatter (FSC). The FSC is roughly proportional to the size of the cell / particle. Generally, larger cells refract more light than smaller cells. Light measured at approximately a 90-degree angle to the excitation line is called side scatter (SSC). The SSC channel provides information about the relative complexity of the cell or particle, such as granularity and internal structure. FSC and SSC are all unique for each cell or microparticle, and the combination of the two can be used for roughly distinguishing the cell type in heterogeneous samples (such as but not limited to blood, sputum).When cell or microparticle pass through laser beam, event is identified, and produces the signal as a function of time.For FSC and SSC, the time that cell or microparticle spend in laser is measured as the width " W " of event, and the maximum height of the current output measured by photomultiplier tube is height " H ", and area " A " represents the integral of the pulse produced by the cell of the inquiry point of the laser beam in the cytometer or microparticle.Each cell and microparticle used in this article can be recorded as an event when passing through the light beam in phototube.

[0088] Now please refer to Figure 1C , which shows a light scatter profile (where forward scatter (FSC) represents cell size and side scatter (SSC) represents granularity), where "A" represents the integral of the pulses generated by cells or microparticles passing through the interrogation point of the cytometer. Figure 1D is the obtained histogram, where the y (axis) represents the laser pulse intensity (H), the x (axis) represents the time (W), and the area under the curve is represented by (A). Figure 1EShown is a plot of SSC-A versus FSC-A for cells of varying granularity and size. The graph is a plot of light scatter distribution (where forward scatter (FSC) represents cell size and side scatter (SSC) represents granularity), where "A" represents the integral of the pulse generated by a cell or particle passing through the interrogation point of the cytometer.

[0089] Light scattering gate for enrichment of RFC

[0090] Specialized airway epithelial cells and glandular cells lining the bronchial tubes secrete mucus. Mucus produced deep in the lungs may contain a large number of cells recovered from lung tissue, including epithelial cells, alveolar cells, macrophages, and other hematopoietic (blood) cells (17). This mucus also contains non-cellular material, which is particularly evident in the lungs of people who smoke, live in highly polluted areas, or are exposed to other respiratory allergens such as pollen. Mucus originating from the lungs is called sputum when it is coughed up. Sputum is often mixed with saliva produced in the mouth, which contains many BECs (or buccal epithelial cells), adding another cellular component to an already complex tissue sample (see Figure 1).

[0091] In contrast to microscopy, flow cytometry can provide multidimensional and / or more precise information about the cell populations in sputum because it allows for the elimination of debris and cells of no interest based on size, granularity, and / or fluorescent labeling, thereby enriching the sample for cells of interest. To enrich for red fluorescent cells in sputum cell analysis, the first step is to standardize the size (diameter) of the red fluorescent cells; any smaller or larger red fluorescent cells are excluded. RFCs are the cells with the highest TCPP uptake, i.e., cancer cells and cancer-associated macrophages, as these two cell types absorb more TCPP than any other cell type (18-22). Lung cancer cells may vary in size depending on the type of cancer, but are unlikely to differ significantly from cultured lung cancer cells. For example, a literature search (Table 1) indicated that HCC15 lung cancer cells have a diameter of 20-30 μm, while alveolar macrophages have been measured to have a diameter of 21 μm. Of particular interest here are macrophages and lymphocytes, as specific subsets of each cell type are known to alter their function when associated with cancer (23-26). However, RBCs (6-8 microns) and any smaller material (debris) as well as BECs (65 microns) and any larger material were excluded from further analysis.

[0092] Table 1.

[0093]

[0094] Now please refer to Figure 2A -I, wherein a flow cytometry analysis profile of cells with SSC and FSC characteristics is shown. Figure 2A and Figure 2G) and cells ( Figure 2B -F, Figure 2H and Figure 2I ) of flow cytometry analysis dot plots ( Figure 2A -F) and contour plots ( Figure 2G -I). Figure 2A From left to right, light scattering plots for 5, 10, 20, 30, and 50 micron beads are shown. The size of each bead was manually plotted on the horizontal FSC axis and imported into Figure 2B -F. The SSC initially remains low, so a higher SSC than expected is observed. Figure 2B Use CellMask TM Light scatter plot of Orange-stained red blood cells (RBCs). Figure 2C Use CellMask TM Light scatter plot of Far Red stained white blood cells (WBC). Figure 2D Use CellMask TM Light scatter plot of Orange-stained squamous lung cancer cells (HCC15). Figure 2E Use CellMask TM Light scatter plot of Green-stained buccal epithelial cells (BEC). Figure 2F Are white blood cells that are analyzed together in a test tube (such as Figure 2C As shown in the positioning), HCC15 cells (as shown in Figure 2D Positioned as shown) and BEC cells (as shown Figure 2E Light scattering pattern of the PDMS detector (positioned as shown). Figure 2F The striped boxes in the figure represent light scatter gates containing the cells of interest; they include all material between 5 and 30 μm in size. Figure 2G Beads of 5 microns (below) and 30 microns (above) are shown in the FSC-W x SSC-W light scattering contour plots. Figure 2H Use CellMask TM Green-stained BEC (eg Figure 2E FSC-W x SSC-W light scattering contour plot of the 144 samples (shown). Figure 2I The combined cell population (WBC, BEC, and HCC15) is shown in the FSC-W x SSC-W light scatter contour plot. The separation between BECs (cells larger than 30 microns and outside the dashed box) and cells of interest (cells smaller than 30 microns and within the dashed box) is clearly visible. The dashed box represents the W x W gate and identifies the population of interest to exclude most BECs.

[0095] In one embodiment, debris and BECs are excluded from the cell population to be further analyzed. Standard sized beads (5, 10, 20, and 50 microns) are used in the light scatter profiles (where forward scatter (FSC) represents cell size and side scatter (SSC) represents granularity; Figure 2A To confirm that these microbeads can indeed predict cell size based on the information provided in Table 1, the microbeads were compared with RBCs, WBCs, and BECs isolated from healthy volunteers and cultured HCC15 lung cancer cells. TM Dye labeling allows for independent analysis ( Figure 2B -E) and combined analysis ( Figure 2F ).like Figure 2B As described and expected from the literature (Table 1), RBCs correspond to the smallest beads. Similarly, WBCs vary in size from approximately 10 to 20 microns ( Figure 2C ), while most HCC15 cells are less than 30 μm in diameter ( Figure 2D When saliva (which mainly consists of BECs) was analyzed by flow cytometry, contrary to expectations, as informed by the literature, most BECs were inferred to be cells under 30 microns ( Figure 2E These results indicate that size can be used to exclude debris (by eliminating all material with beads less than or equal to 5 microns in size), but not to exclude BECs.

[0096] BECs exhibit very high SSC characteristics, which distinguishes them from WBCs and HCC15 cells ( Figure 2F Flow cytometry converts SSC and FSC into electronic signals with height (H), width (W), and area under the curve (A) values. By observing various combinations of SSC and FSC parameters, SSC-W and FSC-W, a distribution graph is generated that allows the elimination of most BECs by setting a gate around cells that exhibit an SSC-W lower than that of 30 micron beads ( Figure 2G -I).

[0097] Subdividing hematopoietic cells into discrete populations

[0098] Another aspect of sputum analysis by flow cytometry is the characterization of various hematopoietic (blood) cell populations. The commonly used WBC marker CD45 is expressed on the cell surface of all WBCs. Using a probe (e.g., antibody) against the CD45 antigen, hematopoietic cells (CD45 阳性 cells) and other cells (including normal lung epithelial cells and potential lung cancer cells (CD45 阴性To identify specific hematopoietic subsets in sputum, we used additional probes, such as antibodies against granulocytes (CD66b), macrophages (HLA-DR, CD11b, CD11c, CD206), and lymphocytes (CD3 and CD19). Table 2 shows exemplary probes and fluorophores.

[0099] Table 2.

[0100]

[0101] Now please refer to Figure 3A -K, which shows the identification and characterization of hematopoietic cells in sputum. Figure 3A Shown are sputum cells presented in a light scatter plot of FCS-A versus SSC-A. The black balls with numbers on the x-axis represent the size of the beads used to set the light scatter gate, which excludes debris and BECs, i.e., all material smaller than 5 micron beads (vertical line on the left) and larger than 30 micron beads (vertical line on the right). Figure 3B Shown Figure 3A Figure 2. FSC-W x SSC-W contour plot of cells within the light scatter gate (where "W" represents the width of the signal). The 30 μm size exclusion gate is indicated by a horizontal line, so any cells detected in the upper box are larger than 30 μm. Figure 3C The FSC-A vs. FSC-H dot plot is shown, where cells are Figure 3B The W x W gate is shown, where "H" represents the maximum amount of current output by the photomultiplier tube detecting light from the cytometer's laser. The gate rectangle shown includes all single cells but excludes doublets. Figure 3D Shows the previous Figure 3A -C Dot plot of light scatter gated sputum cells stained with PE-isotype control to define the CD45-specific gate (indicated by the upper box). Figure 3E Shows the previous Figure 3A -C is a dot plot of sputum cells selected by light scatter gate, where these cells were stained with anti-CD45-PE antibody. All cells expressing CD45 antigen (CD45 阳性 cells) are captured in the upper box. Then further analysis of CD45 阳性 The cells in the upper frame / gate are expressed for CD66b. Based on staining with FITC-isotype control, Figure 3F Background fluorescence of anti-CD66 antibody is shown in FIG. Figure 3G CD45 stained with anti-CD66b is shown 阳性 cells. CD45 阳性 CD66 阳性The cells are indicated by the boxes on the upper side. Figure 3H Shown from Figure 3G Wright-Giemsa staining of sorted cells in the box on the upper side. Figure 3I A dot plot of unstained sputum cells selected only by the BSE gate is shown. This particular sample shows a large subpopulation of cells falling within the box representing intermediate staining in the PE channel, which is used to detect CD45 expression. The presence of this subpopulation makes it difficult to determine the specific subpopulations used to separate the sample into CD45 阴性 cells and CD45 阳性 Cut-off point for cells. Figure 3J Shown with Figure 3I The cells in the lower box (WxW gate) are the cells of interest, while the cells captured in the upper box are SEC, which need to be excluded to visualize the true unstained sputum population of interest. Figure 3K Unstained sputum cells selected by BSE and WxW gates are shown: the negative population is clearly discernible, CD45 阴性 The mean fluorescence intensity of the gate is below the “Gate” horizontal line.

[0102] Figure 3 shows representative samples obtained from patients at high risk for lung cancer. The first two distribution plots in the upper panel ( Figure 3A and Figure 3B ) show light scatter gates for excluding debris and BECs, respectively. A gate for excluding doublets of cells ( Figure 3C ) is an additional doublet cell classification gate. The cells falling in the diagonal box are single cells (SC). The distribution diagram on the upper right ( Figure 3D ) shows cells selected by the three previous light scatter gates (eliminating debris, BECs, and doublets) that were stained with a PE-labeled isotype control antibody to determine background staining with the PE-labeled CD45 antibody. Specific CD45-PE staining in this sample is shown in Figure 3E As shown in Figure 2, CD45 阳性 Cells are marked with upper boxes. Figure 3F Co-staining of CD45 with FITC-labeled isotype control antibody is shown in 阳性 Sputum cell populations, in Figure 3G FITC-labeled CD66b antibody is shown in FIG. 阳性 Cells Figure 3G To confirm that these cells are granulocytes, CD45 阳性 CD66b 阳性 The cells were transferred to slides by cell centrifugation and stained with Wright-Giemsa stain. Figure 3H As shown, CD66 阳性The blood cells recognized by the antibody are indeed granulocytes.

[0103] The remaining CD45 阳性 CD66b 阴性 The cells present may include all other hematopoietic cell types but are most likely macrophages and monocytes or lymphocytes, as other hematopoietic cell types are less abundant in sputum (17,27). Specific markers for macrophages confirm the presence of Figure 4A The majority of cell populations in the 阳性 CD66b 阴性 macrophages / monocytes because they express HLA-DR and / or CD11b.

[0104] Now please refer to Figure 4A -G, which shows CD45 exposed to CD66b probe or CD206 probe 阳性 Sputum cells. Figure 4A -E shows CD66b in various macrophage populations 阴性 group. Figure 4A CD45 阳性 CD66b 阴性 The sputum cells expressed HLA-DR and, in some cases, CD11b. Figure 4A CD45 is shown 阳性 CD66b 阴性 Dot plots of sputum cells stained with an isotype control to determine background staining with anti-HLA antibodies. Figure 4B The histogram also shows the same isotype control staining as a light grey curve (I). Figure 4B The dark grey curve in (C) represents HLA-DR staining of the same cells. A rightward shift of the dark grey curve compared to the light grey curve indicates that the cells are positive for HLA-DR staining. Figure 4C An isotype control for determining background staining with anti-CD11b antibodies is shown. 阳性 CD66b 阴性 The cell population was divided into small (S) cells and large (L) cells, so that CD11b staining could be performed separately. Figure 4D and Figure 4E The isotype control (I) is represented by the light grey curve in the "S" and "L" histograms, while the anti-CD11b antibody staining (C) is shown by the dark grey curve in the "S" and "L" histograms. Only small cells stain positive for CD11b. Figure 4F -G shows CD45 阳性 Isotype control (left dot plot) and CD206 staining (right dot plot) of sputum cells. Figure 4A -B shows the CD45阳性 CD66b 阴性 of sputum cells. Figure 4A CD45 阳性 CD66b 阴性 The sputum cells express the HLA-DR determinants and, in some cases, CD11b, a marker found on myeloid cells.

[0105] In another embodiment, combining the CD3 / CD19 marker with the CD66b marker enables identification of macrophage / monocyte populations (CD66b) in samples that also happen to contain a distinguishable lymphocyte population. 阴性 / CD3 阴性 / CD19 阴性 Potential lymphocyte contamination (28-30) in the cell subsets) was detected. 阳性 CD3 阳性 / CD19 阳性 / CD66b 阳性 Cell populations that are enriched for TCPP are another approach to improve the signal associated with TCPP labeling.

[0106] Now please refer to Figure 5 , which shows CD206 阳性 The presence of cell populations is consistent with the presence of a large number of macrophages on sputum smears. The presence of macrophages in 15 sputum samples was analyzed independently by Wright-Giemsa staining of sputum smears and CD206 staining on a flow cytometer. It should be noted that Wright-Giemsa staining of sputum smears can be substituted with Papanicolaou staining. Plotted are the number of macrophages counted for each slide (solid dots with x) and the CD45 count for each of the 15 samples analyzed. 阳性 CD206 阳性 The percentage of cells with CD206 expression (solid dots) is shown. A black dashed line is added to indicate that the data represent the same sample. Hollow white dots indicate the absence of macrophages on the slide, and hollow white dots with x indicate uncertain CD206 distribution. Figure 5 As shown, when a large number of macrophages were identified on sputum smears, CD45 阳性 CD206 阳性 When there are no or very few macrophages on the slide, CD45 阳性 And CD206 阳性 The distribution of cells in sputum is unreliable. 阳性 CD206 阳性The presence of cell populations (regardless of size) consistent with the large number of macrophages (>13) observed on the slide indicates that the sputum sample is of higher quality (ie, deep lung). 阳性 CD206 阳性 If the cell population was not clearly visible (samples 2, 10, and 11) or difficult to identify (samples 3 and 4), the sputum smear showed zero to a small number of macrophages (≤13), indicating that the sputum sample was of poor quality. The presence of macrophages in 15 sputum samples was analyzed independently by Wright-Giemsa stained sputum smears and CD206 staining on a flow cytometer. Plotted are the number of macrophages counted for each slide (solid dots with X) and the CD45 count for each of the 15 samples analyzed. 阳性 CD206 阳性 The percentage (%) of cells with CD206 (solid dots) is shown. A black dashed line is added to indicate that the data represent the same sample. The absence of macrophages on the slide is indicated by a hollow dot, and an undefined CD206 distribution is indicated by a hollow white dot with an X.

[0107] pass Test identifies cancer cells in sputum

[0108] Another component of flow cytometry-based sputum analysis for early cancer detection is the expression of cancer cells. We analyzed sputum samples obtained from high-risk patients (who may not have lung cancer) and added approximately 3% HCC15 cancer cells to the samples. For this experiment (e.g. Figure 6 As shown), use CellMask TM Green labels HCC15 lung cancer cells, allowing all cancer cells in the mixture to be identified by this green color. Sputum cells were stained with anti-CD45-PE antibodies, which allowed the separation of hematopoietic cells from non-hematopoietic cells (including CD45 阴性 After cell fixation, the cell mixture was labeled with TCPP and the cells were analyzed by flow cytometry.

[0109] Please refer to Figure 6 , which shows the experimental setup for analysis of sputum spiked with lung cancer cells. TM Green was used to label HCC15 cancer cells (step 1), while in another tube, PE-labeled anti-CD45 antibody was used to stain dissociated sputum cells (step 2). TM After staining with Green solution and anti-CD45 antibody, the two cell suspensions were mixed (step 3). The mixed cell suspension was then fixed and stained with TCPP as a fluorescent component. Incubate with the solution (step 4). Figure 6 Flowchart showing the preparation of sputum samples for analysis. TM Green was used to label HCC15 cancer cells (step 1), while in another tube, PE-labeled anti-CD45 antibody was used to stain dissociated sputum cells (step 2). TM After staining with Green solution and anti-CD45 antibody, the two cell suspensions were mixed (step 3). The mixed cell suspension was then fixed and stained with TCPP as a fluorescent component. Incubate with the assay solution (step 4).

[0110] Now please refer to Figure 7A -C, which shows a dot plot of sputum cells labeled with CD45-PE, a green cell mask, and treated with TCPP, wherein lung cancer cells (HCC15) were spiked into the sample. Figure 7A Figure 2 is a representative dot plot of CD45 expression on sputum cells spiked with approximately 4% HCC15 lung cancer cells. 阴性 ) has previously used the green fluorescent dye CellMask TM Green mark (see Figure 6 ). Indicates CD45 阳性 The upper side of the cells is gated based on appropriate isotype controls (see Figure 7D ). The bottom gate represents CD45 阴性 non-hematopoietic cells. Figure 7B TCPP staining (y-axis) and CellMask are shown TM CD45 Green staining (x axis) 阳性 Dot plot analysis of cells. In the upper left corner box, CD45 is clearly visible. 阳性 These cells are likely macrophages that stain positive for TCPP. Figure 7C TCPP staining (y-axis) and CellMask are shown TM CD45 Green staining (x axis) 阴性 Dot plot analysis of cells. CellMask TM Green 阳性 The cells are HCC15 cells spiked into sputum samples and all stains are TCPP positive (upper right quadrant). CellMask TM Green 阴性 The cells are sputum cells, showing 1.2% background staining (lower left quadrant). Figure 7A After the three light scatter gates shown in -C were applied to the mixture of sputum cells and HCC15 cells, the CD45 expression of the cells was analyzed ( Figure 7A Then, in CD45 阳性 Cell populations (marked with upper boxes) and CD45 阴性 TCPP uptake was measured in the cell population (marked by the lower box). Only a small fraction of CD45 阳性 Cells showed TCPP uptake ( Figure 7B ). In contrast, CD45 阴性 Cells display very discrete TCPP 阳性 Cell populations, which are sensitive to CellMask TM Green staining was also positive ( Figure 7C The upper right quadrant of ). Due to the use of CellMask TM Green-treated cells are only HCC15 lung cancer cells, so TCPP 阳性 And CellMask TM Green 阳性 The cells are HCC15 lung cancer cells spiked with CellMask. TM Green 阳性 cell( Figure 7C lower right quadrant of ), which indicates All cancer cells spiked into the sputum samples were stained.

[0111] In a small pilot study, five sputum samples were analyzed: one sample from a healthy volunteer, three samples from high-risk patients without cancer, and one sample from a lung cancer patient. The analysis was performed as shown in Figure 7, meaning that each sample was spiked with the CellMask TM Green-labeled HCC15 cells were analyzed as shown in Figure 7. The rationale for adding C15 cells to the samples is that these cells will serve as Although there was only one C sample among the five samples analyzed, the data showed that the sputum sample from the lung cancer patient was different from the sputum sample obtained from another patient without lung cancer: the C sputum sample contained more CD45 than the sample collected from the cancer-free individual. 阴性 cells and less CD45 阳性 cell( Figure 8A ). Most importantly, in CD45 阴性 Among the (epithelial) cell populations, sample C showed the highest amount of TCPP 阳性 cells. CD45 阳性 The TCPP markers in the population could not uniquely distinguish C samples from other non-cancer samples ( Figure 8B ) to distinguish them.

[0112] Now please refer to Figure 8A-B, which shows a preliminary comparative analysis of sputum samples obtained from healthy volunteers and high-risk patients with and without cancer. Figure 6 Similar to the experiment detailed in Figure 7, five samples from different donors were analyzed. The open dots represent samples from healthy volunteers (H), the black dots represent samples from high-risk patients (HR) without cancer, and the dots with an x represent samples from patients diagnosed with lung cancer (C). Figure 8A The expression of CD45 in each sample analyzed is shown. 阴性 (Left) and CD45 阳性 Total number of cells (right). Figure 8B The expression of CD45 in each sample analyzed is shown. 阴性 (Left) and CD45 阳性 (Right) TCPP in cells 阳性 The proportion of cells.

[0113] Now please refer to Figure 9A -F, which shows a method for analyzing the presence of TCP in sputum cells according to an embodiment of the present invention. 阳性 Dot plot of a strategy for cells. Figure 9A A dot plot of a mixture of sputum cells mixed with HCC15 cells treated with anti-CD45-PE antibody is shown. The upper gate includes CD45 阳性 cells, and based on appropriate isotype controls (not shown). The lower gate represents CD45 阴性 non-hematopoietic cells. Figure 9B Shown are cells treated with a mixture of TCPP and FITC-labeled probes. FITC-labeled probes included antibodies against CD66b (granulocytes), CD3, and CD19 (lymphocytes). Figure 9B There are four quadrants: cells above the horizontal line are TCPP-positive, while cells to the right of the vertical line are FITC-positive. The circles drawn represent the different cell populations present in the sample. Figure 9C Shows the Figure 9B Analysis of the same cells in , the relationship between FITC intensity (y-axis) and FSC-A (x-axis; representing cell size) is shown in the dot plot. Figure 9B and Figure 9C Cell populations are identified between them. Figure 9B The cells in the lower right quadrant of the image show a distribution consistent with granulocytes, while the cells in the upper right quadrant show a distribution consistent with alveolar macrophages. Figure 9D The CD45 阴性 The relationship between TCPP labeling (y-axis) and FITC fluorescence intensity (x-axis) of sputum cells. 阴性The composition contains HCC15 cells, so we expect to find a large amount of TCPP in this plate 阳性 There are two TCPP cells in this sample. 阳性 groups, as shown by the circle in the upper left quadrant and the circles in the middle and upper right quadrants. Figure 9E Shown as Figure 9D CD45 阴性 The distribution of cells was similar to that of HCC15 cells, but the cells were from a control sample that did not contain HCC15 cells spiked into the sample. Figure 9D The cell population in the upper left quadrant of Figure 9E The cells missing from the upper left quadrant of the distribution dot plot (empty circle) are HCC15 cells. Figure 9F Shown with Figure 9D The same cell population, where the dot plot shows the relationship between CD45-PE intensity (y-axis) and FSC-A (x-axis). Figure 9F Marked in Figure 9D and Figure 9E The upper left cell group, the upper right cell group, and the central cell group.

[0114] Figure 9 shows that CD45 阳性 TCPP staining in cells is associated with alveolar macrophage populations. According to the FITC channel and TCPP ( Figure 9B ) in fluorescence intensity, CD45 阳性 (Hematopoietic) cell block Figure 9A ) are subdivided into three cell subsets. When restored on the CD66b / CD3 / CD19 and FSC distribution graph, Figure 9B The cell population not stained with TCPP, which is circled at the lower right in the figure, appears as smaller cells that stain positive for the CD66b / CD3 / CD19 mixture ( Figure 9C ); these cells are probably granulocytes. Figure 9B The other FITC-positive cell group in the image (the circled cell group on the upper right, TCPP-positive) is a larger cell. Their green fluorescence is most likely due to autofluorescence rather than the previous Figure 3F The distribution of isotype controls is shown by CD66 / CD3 / CD19 staining. The large size and high autofluorescence suggest that the cell population on the upper right may be alveolar macrophages (35,36). Figure 9B The cell population on the lower left is composed of smaller cells, and since this subpopulation is also CD66 / CD3 / CD19 阴性 CD45 阴性 cell( Figure 9CHere, we compared HCC15 cells spiked into samples with similarly treated aliquots that did not contain spiked HCC15 cells (Comparison Figure 9C and Figure 9D The HCC15 lung cancer cell population that was not spiked in the sample is marked with a circle. TCPP-positive cells are medium-sized cells that do not express CD45. Figure 9E For samples without HCC15, there is no cell population in the circle in the upper left corner, which confirms the TCPP staining characteristics of the HCC15 cell population ( Figure 9E ). CD45 阴性 Another TCPP in sputum cells 阳性 The cell population (indicated by the middle / upper right circle) contains cells of similar size to HCC15 cells ( Figure 9F These cells are also CD45 阴性 However, they can be distinguished from HCC15 cells by low levels of autofluorescence in the FITC channel ( Figure 9D and Figure 9E ).

[0115] Now please refer to Figure 10A -B, use quality control beads to establish Figure 10B Bead size exclusion (BSE) gate within the dot plot. Figure 10B Sputum samples were gated to remove cells from the analysis that fell to the left of the gate around the approximately 5 μm bead size and to the right of the gate around the approximately 30 μm bead size. Sputum samples, controls, isotype controls, and beads were prepared as described in the experimental protocol.

[0116] Now please refer to Figure 11A -F, Treated and untreated sputum samples were analyzed by flow cytometry, and the resulting dot plots are shown. Untreated sputum cells were first size-gated using a BSE gate to select cells larger than about 5 microns and smaller than about 30 microns for further analysis. Figure 11A A dot plot of sputum cells falling within a size range is shown. This size gate is called the BSE gate. The BSE gate excludes debris and red blood cells, but does not exclude squamous epithelial cells (SEC). Since SEC are dead cells, they are eliminated from the analysis of sputum samples stained with the vital dye FVS510. Figure 11B -C shows the unprocessed ( Figure 11B ) and treated with BV510 fluorescence ( Figure 11C ) of sputum cells and forward scatter. Sputum cells that do not absorb the dye are viable cells (LC). Figure 11CBelow the line. This live cell gate is called the LC gate. The dye stains dead cells; live cells are those that are not stained by FVS510. Although FVS520 dye was used in this example, other viability dyes can also be used to distinguish LC populations. The threshold above which cells are considered FVS510 positive (and therefore dead) is based on an unstained control sample ( Figure 11B ). The majority of cells (over 95%) of the unstained control should fall within the LC gate, and less than 5% of the cells ("background staining") should fall outside the LC gate. When this LC gate is applied to a sputum sample stained with FVS510, live cells are those within the LC gate, and dead cells fall outside the gate.

[0117] Figure 11D It is a dot plot of an unstained sputum sample used to identify single cells and doublets. Flow cytometry considers doublets as one event, which may contain amounts of TCPP that represent two or more cells. Therefore, because TCPP is used as a marker for cancer cells, doublets can produce events with artificially high TCPP content and give false indications of cancer cells or cancer-related cells. In order to eliminate doublets, a gate was drawn to identify the single cell (SC) population. FSC-A and FSC-H dot plots of sputum cell distribution were generated from the acquisition results, and a BSE / LC gate was applied to analyze the SC population. Two diagonal lines were drawn along the axis of the main population: one along the top (at Figure 11D The bottom diagonal is somewhat parallel to the top diagonal and preferably starts at a "gap" in the cluster where cells appear to spread out from the main cluster to the right (not shown). Cells that spread out (i.e., cells or dots that do not fit into the diagonal cluster) are doublets and need to be excluded from the analysis. The SC gate will only include cells that form diagonally oriented clusters. Figure 11D , SC cells are shown within the diagonal gate. This SC gate is generated by connecting two diagonals: one along the top of the main population (indicated by the "top diagonal") and the other along the bottom of the main population (the "bottom diagonal"). In order to set the bottom diagonal, a "gap" needs to be marked in the dot plot, which indicates the starting point of the cells that do not conform to the diagonal main cell population. Below and to the right of the bottom diagonal (the light gray area) contain double cell clusters that will be excluded from the SC gate. The bottom diagonal needs to pass through the gap as it extends up and down following the main diagonal.

[0118] Figure 11E -F shows dot plots of sputum cells treated with PE control or CD45 probe conjugated to a PE fluorophore. Figure 11E is an isotype control. Figure 11F Identify cells as CD45 阳性 (blood cells) or CD45阴性 (non-blood cells) and is called the CD45 gate.

[0119] The first sputum sample from the subject was processed with a CD45 probe conjugated to a fluorophore, a mixed probe of CD66B, CD3, and CD19 conjugated to a fluorophore, and a CD206 probe conjugated to a fluorophore and TCPP (tube #6). Figure 12A -C shows the results of the experiment by applying BSE, LC, SC and CD45 gates to select CD45 treated with CD66b / CD3 / CD19-FITC-Alexa488 and CD206-PE-CF594 labeling. 阳性 Dot plot of sputum cells selected for sputum cells. Only cells that met the criteria of the applied gate were further analyzed. Cell populations were identified based on the intensity of the fluorophores along the CD206 antibody (x-axis) and CD66b / CD3 / CD19 (y-axis). In each sample, 5 to 6 populations could be identified. The relative size of each population varied between samples. Figure 12A Distribution Graph 1 is shown where Group 1 dominates. Figure 12B Distribution Graph 2 is shown where Group 2 dominates. Figure 12C CD206 is shown 阳性 (CD206 + ) cells are dominant (i.e., groups 3 to 6). The dominant group in each type of distribution diagram is indicated by a bold frame. 阳性 Three different characteristics of sputum cells. Based on the isotype control and control sputum samples, 5-6 cell groups were determined, as further shown in the figure below. The presence of macrophages indicates that the sample is from the deep lung. Table 3 lists the types of cells present in each group.

[0120] Table 3

[0121]

[0122]

[0123] Now please refer to Figure 13A -B, shows dot plots of an isotype control of FITC / ALEXA-488 and sputum cells treated with a CD66b / CD3 / CD19 probe conjugated to FITC / Alexa488. Figure 13A CD45 stained with FITC / Alexa488 isotype control is shown 阳性 Dot plot of cells with FSC shown on the x-axis and FITC / Alexa488 shown on the y-axis. Figure 13B CD45 stained with a cocktail of antibodies against CD66b / CD3 / CD19-(FITC / Alexa488) and CD206-(PE-CF594)阳性 Dot plot of cells (with Figure 11A The horizontal FITC / Alexa488 gate was set based on cells above background staining. The negative gate in the isotype control was set to include approximately 95% of the cells in the isotype control, while the positive gate was set to include approximately 5% of the background. - The highest value of the FITC / Alexa488 negative gate in cells was an average of 450, ranging from 100 to 1000.

[0124] Now please refer to Figure 14A -B, dot plots of PE-CF594 isotype control and sputum cells treated with PE-CF594 labeling are shown. Figure 14A CD45 stained with isotype control is shown 阳性 Dot plot of cells with FSC shown on the x-axis and PE-CF594 shown on the y-axis. Figure 14B CD45 was stained with a probe / antibody conjugated to PE and directed against CD206 cell marker 阳性 Dot plot of cells (with Figure 14A similar). Figure 14B The gate above shows the cell population positive for the CD206 marker. - The highest value of the PE-CF594 negative gate in cells was 250 on average, ranging from 90 to 500.

[0125] Now please refer to Figure 15A -B, which shows the dot plot for setting the double-negative gate or population 1. Figure 15A CD45 stained with FITC / Alexa488 and isotype control PE-CF594 (Texas-Red) channels are shown. 阳性 Dot plot of sputum cells with FITC / Alexa488 shown on the y-axis and PE-CF594 (Texas-Red) shown on the x-axis. Figure 15B is with Figure 15A Same dot plots shown, showing the expression of BSE, LC and CD45 阳性 Pseudo-color image of isotype control gated with the cell gate. The horizontal dashed line represents the FITC / Alexa488 positive / negative cutoff value determined in Figure 13, while the vertical dashed line is derived from the PE-CF594 positive / negative cutoff value determined in Figure 14. The gate of population 1 as determined in Figure 15 was shifted to the gates of the cell gates as shown in Figure 15, respectively. Figure 16A and 16B CD45 stained with antibodies against CD66b / CD3 / CD19 (FITC / Alexa488-y axis) and CD206 (PE-CF594-x axis) as shown 阳性In the whole-dot and pseudo-color images of sputum cells, CD45 阳性 In cells, FITC / Alexa488 阴性 The highest gate value was an average of 600, with a range of 200-1050. In most samples, CD45 阳性 Among cells, the highest value of the PE-CF594 negative gate was 500 on average, ranging from 200-750.

[0126] Now please refer to Figure 16A -B, which shows a dot plot of the sputum sample as shown in Figure 15, where CD45 阳性 Cells were stained with a mixture of CD66b / CD3 / CD19 antibodies conjugated to FITC / Alexa488 and CD206 antibodies conjugated to PE-CF594 and analyzed for the presence of different cell populations. 阳性 After gating, the cell population identified as 1–5 still remained. Figure 16A The same group 1 (box) and cutoff point (dashed line) of are shown in Figure 15 and applied to Figure 16A -The distribution diagram shown in B.

[0127] Figure 16B The gates for populations 2-6 were established. Populations 3, 5, and 6 are FITC autofluorescent and should fall above the horizontal dashed line, as shown in Figure 1. Figure 16A As shown. Group 4, which is not autofluorescent in FITC, should fall below the dotted line, as shown. Figure 16A Because population 2 is characterized by cells that are negative for CD206 (similar to population 1) and positive for CD66b / CD3 / CD19, the gate for population 2 was drawn above population 1 and to the right of the PE-CF594 cutoff point, which is Figure 16A The box above group 1 formed by the solid and dotted lines is in Figure 16B Group 5 can be identified as a completely isolated group on the right side of the distribution graph, i.e., PE-CF594 阳性 FITC 阳性 Group ( Figure 16B , gate for group 5). Sometimes, group 5 is mid-FITC / Alexa455 阳性 In this case, the gate for isolate 5 crosses the horizontal red dashed line (see Figure 17A ).

[0128] Now please refer to Figure 17A -C, which shows the expression of CD45 阳性 Pseudo-color dot plots of two sputum samples treated with CD66b / CD3 / CD19-FITC / Alexa488 probes ( Figure 17A-B are the same samples but showing different gates). All dot plots show CD45 gated by BSE, LC, and SC gates. 阳性 Sputum cells. Horizontal and vertical dashed lines are set on isotype controls (not shown). Figure 17A -B shows the situation in the graph of gates 4 and 5 when the FITC mean fluorescence intensity of population 5 is in the middle and crosses the cutoff line. Figure 17C Population 6 is shown in the upper right box.

[0129] Now please refer to Figure 18 , each () on the x-axis reflects the Figure 12A For distribution plot 1, the proportion of each cluster (cluster 1, cluster 2, cluster 1+2, cluster 3+4+5+6) in high-risk (HR) sputum samples was plotted as the percentage of all CD45 阳性 The median of the percentage of cells (%) is shown in Figure 2. The median of each group of the distribution diagram group is connected by a straight line. Figure 18 The features of profile 1 were generated by drawing a line between the medians of each cluster identified for profile 1. Features of profiles 2 and 3 were similarly generated for sputum samples from subjects at high risk for lung cancer and subjects identified as having lung cancer.

[0130] Now please refer to Figure 19A -C, which shows a comparison of blood cell characteristics in sputum collected from subjects at high risk for lung cancer (HR) and subjects identified as having cancer (C). Figure 19A Shown from Figure 18 The distribution of features 1 (feature 1). Figure 19B The feature of profile 2 (feature 2) is shown. Figure 19C A feature (Feature 3) of profile 3 is shown. For each feature of the HR and C sputum samples, the percentage (%) of cells in population 6 was determined and identified.

[0131] Figure 20A -D shows a dot plot of sputum cells treated with a mixture of CD45 and panCytokeratin-Alexa488 and EpCAM-PE-CF594data and TCPP according to tube #7. The cells depicted in the dot plot are the cells remaining after applying the BSE, LC, SC, and CD45 gates. Further analysis was performed on (CD45 阴性 ) Cell dot plots of distribution graphs 1-4, all CD45 阴性 The percentage of cells and the relative TCPP fluorescence intensity represented by each population.

[0132] In each sample, nine populations were identified, such as Figure 20AFor each of the distribution graphs 2-4, the same nine populations were identified. The relative size of each subpopulation varied from sample to sample, and each subpopulation had a different distribution graph (distribution graphs 1-4). Figure 20A A distribution diagram is shown in which population 1 is dominant and contains all CD45 阴性 More than 80% of cells. Figure 20B A distribution diagram is shown where population 1 also predominates but contains all CD45 阴性 Less than 80% of the cells; there are often distinct clusters of cells within one of the other gates. Figure 20C A distribution diagram is shown where Group 1 is still large (although less than 80%), but the second largest group is Group 2. Figure 20D A distribution diagram is shown in which group 5 is the most important group or the second most important group after group 1. Each distribution diagram has different characteristics. The most important groups for determining the type of characteristics are shown in bold.

[0133] Figure 21A -B shows CD45 treated with FITC / Alexa488 or panCytokeratin / Alexas488 阴性 Dot plot of isotype control of sputum cells. BSE, LC, SC and CD45 阴性 Two distribution plots were generated: one showing CD45 阴性 cells, where the x-axis is forward scatter-A (FSC-A) and the y-axis is FITC / Alexa488 ( Figure 21A ); another shows CD45 阴性 cells, where the x-axis is FSC-A and the y-axis is panCytokeratin / Alexa488 ( Figure 21B The negative gate in each distribution plot was set to include approximately 95% of the cells in the isotype control. The positive gate in each distribution plot included the remaining space above the negative gate and should contain less than 5% background staining.

[0134] Figure 22A -B shows the isotype control of PE-CF594 and the cells that have been treated with BSE, LC, SC and CD45 阴性 Dot plot of gated CD45-negative sputum cells. Before analysis, this population was treated with BSE, LC, SC, and CD45 阴性 Two distribution plots were generated: one showing CD45 阴性 cells, where the x-axis is forward scatter-A (FSC-A) and the y-axis is PE-CF594 ( Figure 22A ); another shows CD45 阴性cells, where the x-axis is FSC-A and the y-axis is EpCAM-PE-CF594 ( Figure 22B The negative gate in each distribution plot was set to include approximately 95% of the cells in the isotype control. The positive gate in each distribution plot included the remaining space above the negative gate and should contain less than 5% background staining.

[0135] Now please refer to Figure 23A -B, which shows CD45 阴性 Dot plot of double negative gate or population 1 of cells. Figure 23A It's a dot graph. Figure 23B is a pseudo-colored image of an isotype control in which processed sputum samples were analyzed by flow cytometry and by BSE, LC, SC, and CD45 阴性 The cell gate gates events representing cells. Figure 23A The horizontal dashed lines in FIG21 represent the FITC / Alexa488 positive / negative cutoff values determined in FIG21 , while the vertical dashed lines are derived from the PE-CF594 positive / negative cutoff values determined in FIG22 . The cutoff line for population 1 determined in FIG23 was combined with CD45 staining with antibodies against all cytokeratins (Alexa488-y axis) and EpCAM (PE-CF594-x axis). 阴性 In whole-cell and pseudocolor images of cells.

[0136] Now please refer to Figure 24A -B, which shows the CD45 setting of tube #7 阴性 Gate for sputum cell populations 2-9. Figure 24A This is a dot plot of sputum cells. Figure 24B 23 is a pseudo-color image of the same sputum sample, but this time the cells were stained with Alexa 488 labeled antibodies against all cytokeratins (y-axis) and PE-CF594 labeled antibodies against EpCAM (x-axis). CD45 cytokeratin cells were also selected by BSE, LC, and SC gates. 阴性 Cells. As shown in Figure 23, the same population 1 (cells within the solid box) and cutoff point (dashed line extending therefrom) were used for these distribution plots. Cytokeratin ++ Cells are expressed as cells that are highly stained with pancytokeratin antibodies, whereas EpCAM ++ Cells represent cells that are highly stained with the EpCAM antibody. Populations 1, 2, and 3 are EpCAM negative, so they should fall above population 1, to the left of the vertical stripe line between populations 1 and 6. The first three populations are distinguished by their expression of varying levels of pancytokeratin. The cutoff between populations 2 and 3 was determined by identifying cells that were highly stained with panCytokeratin-Alexa488.阴性 The cutoff fluorescence intensity of the cells ranged from 10,000 to 20,000 (average 14,000), and this cutoff value determined the bottom line of cluster 3 as well as clusters 4 and 9. Figure 24A A horizontal streak line is shown that separates population 2 from population 3, and in this particular sample, cells above this streak line are considered to be highly stained with anti-pancytokeratin antibodies. A cutoff value was determined on the pseudocolor plot, and a distinct cell population was identified above the 10,000 fluorescence intensity mark. Populations 1, 6, and 7 are pancytokeratin negative, with populations 6 and 7 falling to the right of population 1, below the horizontal streak line. The difference between populations 1, 6, and 7 lies in the level of EpCAM expressed on these cells. Population 7 was identified as a cell population that highly expresses EpCAM, just like populations 8 and 9. The cutoff value for cells that highly express EpCAM averaged 3,000, with a range of 1,000 to 6,000. Figure 16A The vertical striped line in the figure represents the cutoff for cells that highly express EpCAM, thereby defining the left side of populations 7, 8, and 9. In certain embodiments, cells that highly express FITC use 10,000 as the cutoff for cells that highly express PE-CF594: a value of 10-15x is used to identify the highest value of the PE-CF594-negative gate (or vertical solid and striped lines).

[0137] Figure 25 Sputum cells from tube #7 of a high-risk subject are shown in the presence of BSE, LC, SC, and CD45 阴性 As shown in Figure 20, the dot plot shows the distribution of Figures 1-4 from subjects with a high risk of developing lung cancer, and Figure 26 Further analysis in.

[0138] Figure 26 The non-blood features of the profile 1 (non-blood features 1) are shown, in which each of the clusters (cluster 1, cluster 2, cluster 5 and PanCK) in the same profile depicted in each panel is determined. ++ (CD45 阴性 )) and generate features by drawing a line from the median of each cluster in the distribution. Features are generated for each of the distributions 1-4.

[0139] Figure 27 Non-blood signatures are shown for sputum samples from subjects at high risk (HR) for lung cancer but without the disease (ldct not shown for subsequent C and subjects with lung cancer (C)). In signature 4, note that for the signature of C samples, the arrow at group 5 indicates a decrease in mean EpCAM cell expression, while the arrow at group pCK indicates an increase in mean panCytokeratin expression compared to HR signature 4.

[0140] Figure 28A -B shows the PanCK for group 3+4+9 ++ The sensitivity and specificity of the presence of cells were calculated for all CD45 阴性 The percentage of cells was expressed as ++ Application of the biomarker to sputum samples yielded cancer cell identification with a sensitivity of 80% and a specificity of 85%.

[0141] Figure 29A -C shows the analysis of CD45 in sputum samples 阴性 / CD45 阳性 Analysis of cells in sputum samples obtained from subjects at high risk for cancer and subjects with cancer followed by the proportion of (Biomarker 1) cells. Figure 29A CD45 in sputum samples of high-risk individuals is shown. 阴性 / CD45 阳性 The proportion of cells. Figure 29B CD45 in sputum samples from subjects known to have cancer is shown. 阴性 / CD45 阳性 The proportion of cells. Figure 29C CD45 in sputum samples from two subjects 阴性 / CD45 阳性 Analysis of cell ratios.

[0142] Figure 30A -B shows the results of the experiment on biomarker 1 (CD45 阴性 / CD45 阳性 When sputum samples from HR and C samples were analyzed using the ratio of cells to sputum samples, a specificity of 54% and a sensitivity of 90% were achieved.

[0143] Figure 31A -C shows CD45 in test tube #7 阴性 Dot plots of sputum cells. These sputum samples were obtained from subjects at high risk for cancer and subjects with cancer and were expressed in BSE, LC, SC, and CD45 cells. 阴性 The analysis was performed after gating. The Y-axis is TCPP fluorescence intensity, and the X-axis is panCytokeratin-Alexa488. TCPP is present in CD45-negative cells in biomarker 2, and in panCytokeratin-Alexa488-stained cells. Figure 31A Shown are dot plots of TCPP-labeled cells in sputum samples from high-risk individuals. Figure 31BDot plots of TCPP-labeled cells in sputum samples from subjects known to have cancer are shown. Group B represents the TCPP cell population. Figure 31C is the TCPP in group B from each subject 阳性 CD45 in sputum samples 阴性 Analysis of cell percentages.

[0144] Figure 32A -B shows that in one embodiment of the method of applying biomarker 2 of FIG. 31 to distinguish between lung cancer (C) sputum samples and high risk (HR) (non-lung cancer) sputum samples, a specificity of 63% and a sensitivity of 100% were achieved.

[0145] Figure 33A -C shows the application of the combination of Biomarker 1 and Biomarker 2 to the collected sputum samples identified in Figures 31 and 32 to analyze sputum samples obtained from subjects at high risk for lung cancer and subjects identified as having lung cancer in one embodiment of the present invention. Figure 33C It was shown that 90% sensitivity and 90% specificity were achieved in identifying samples from subjects with cancer or subjects without cancer.

[0146] Figure 34A -C shows cancer risk analysis of cells in sputum samples labeled with CD66b / CD3 / CD19 and CD206 to identify CD66b / CD3 / CD19 in population 6. ++ and CD206 ++ The horizontal gate of group 6 is set between 10,000 and 30,000 mean fluorescence intensity (e.g., between 10,000-15,000, or between 15,000-20,000, or between 20,000-25,000, or between 25,000-30,000). Figure 34C The results are shown in Table 2. Figure 34A ) and subjects identified as having lung cancer ( Figure 34B ) in the sputum samples obtained. 阳性 cells (biomarker 3) compared to the total number of cells in population 6.

[0147] Figure 35A -B shows that in one embodiment of a method of applying the biomarkers of FIG. 34 to distinguish between lung cancer (C) sputum samples and high risk (HR) (non-lung cancer) sputum samples, a specificity of 88% and a sensitivity of 60% were achieved.

[0148] Figure 36A -B shows CD45 in sputum samples collected from subjects at high risk for lung cancer and subjects identified as having lung cancer 阴性Cancer risk analysis of cells. 阳性(或高度表达) CD45 阴性 The percentage of cells was determined as biomarker 4.

[0149] Figure 37A -B shows that in one embodiment of a method of applying the biomarkers of FIG. 36 to distinguish between lung cancer (C) sputum samples and high risk (HR) (non-lung cancer) sputum samples, a specificity of 83% and a sensitivity of 80% were achieved.

[0150] Figure 38A -E shows cancer risk analysis of cells from sputum samples of cancer subjects and high-risk subjects using a combination of biomarkers 1, 2, 3, and 4. When the combination of biomarkers 1, 2, 3, and 4 was applied to sputum samples to distinguish cancer samples from non-cancer samples, a specificity of 98% and a sensitivity of 78% were achieved.

[0151] Figure 39 A flow chart of a screening of a subject's lung health comprising the systems and methods described herein for isolating a cell population from the lung is shown. In a 2014 study, the RFC fluorescence intensity parameter in TCPP-labeled lung sputum combined with the patient's smoking history data was able to classify the participants into cancer and high-risk groups with an accuracy of 81% (12). Enhanced sputum cytology has a higher sensitivity than conventional sputum cytology (77.9%), but the number of cells counted from stained slides (12 slides / patient) (approximately 600,000) is a limiting factor in assay sensitivity. Using a Poisson distribution of RFC in cancer samples, it is expected that the sensitivity of RFC detection can be increased to 95% by simply doubling the number of cells examined to >1 million (12). In addition, the need to include a separate sputum smear step for macrophage quantification to verify sample adequacy facilitates assay designs with lower potential for automation or scalability. Therefore, high-throughput flow cytometry is an alternative to slide-based assays that can support the examination of millions of cellular events within a clinically relevant timeframe.

[0152] Experimental plan

[0153] Human sputum samples

[0154] Volunteers were recruited to provide sputum samples over three days. Three different study groups were included: 1) individuals at high risk for lung cancer but who may not have developed cancer; 2) individuals diagnosed as having high risk for lung cancer; and 3) healthy individuals (over 22 years of age) who have not been diagnosed with cancer and are not at high risk for developing lung cancer. To qualify for the high-risk group, subjects had to be heavy smokers, defined as having a smoking history of ≥30 pack years and be between 55 and 75 years of age (13). (Examples of a 30-pack-year smoking history include: 1 pack per day for 30 years, 2 packs per day for 15 years, etc.) For the healthy group, subjects had to have a smoking history of ≤5 pack years and / or have quit smoking for ≥15 years and be over 22 years of age. Other exclusion criteria (applicable to all groups) were having severe obstructive pulmonary disease, uncontrolled asthma, angina with mild exertion, pregnancy, or working in the mining industry.

[0155] Sputum collection

[0156] All study participants received the drug according to the manufacturer's instructions. Training on the assistive device (manufactured by Smiths Medical, St. Paul, Minnesota). The device is an FDA-approved handheld device that helps dilute and mobilize mucus secretions deep in the lungs. Subjects used the device as directed and expelled sputum samples into a sterile collection cup. Subjects repeated the procedure at home to collect sputum samples on the second and third days. Subjects were instructed to store their specimen cups in a cool, dark place or refrigerator and return them to the initial collection site within 1 day of collection. Completed specimen cups were packaged in frozen shipping ice packs and sent overnight for analysis. In the 3-day collection samples received (n=38), cell viability averaged 64.3% (SD: 25.6%; range: 23.6-100%), excluding buccal epithelial cells (BECs or cheek cells), which were all dead (14).

[0157] Dissociation of sputum

[0158] The sputum plug was separated from the contaminated saliva using a cotton swab (15, 16). In cases where the plug was not an option, the entire sample was processed. The sputum was mixed with preheated 0.1% dithiothreitol (DTT) in a ratio of 1:4 and the sputum plug weight (w / w) was mixed with 0.5% N-acetyl-L-cysteine (NAC) in a ratio of 1:1. The mixture was then shaken at room temperature for 15 minutes. Hank's balanced salt solution (HBSS; manufactured by ThermoFisher Scientific, Waltham, Massachusetts, USA) (4 times the volume of the sputum / DTT / NAC mixture) was added, and the resulting cell suspension was shaken for an additional 5 minutes at room temperature, filtered through a 40-110 micron nylon cell strainer (Falcon, manufactured by Corning Inc.) to remove debris, and centrifuged at 800 x g for 10 minutes. After decanting the supernatant, the cell pellet was resuspended in 1 ml of HBSS. The total cell count was determined using a Neubauer cell counter, and cell viability was determined by trypan blue exclusion.

[0159] Sputum smear

[0160] Sputum cells are transferred to one slide using the same cotton swab used to transfer the sputum block for processing. Using another slide, the sputum sample is spread between the two slides, covering most of both slides (16). The slides are air-dried and then stained with Wright-Giemsa stain. One or both slides are read by a pathologist, and the number of macrophages is counted.

[0161] Other human samples

[0162] blood

[0163] Two 7 ml vials of peripheral blood were obtained from healthy volunteers. Most of the blood was collected by BD PharmLyse TM (manufactured by BD Biolosciences, San Jose, CA, USA) lysed red blood cells (RBCs) to obtain white blood cells (WBCs). The remainder was used as a source of red blood cells.

[0164] saliva

[0165] BECs were collected from the oral mucosa of healthy volunteers by scraping the inner cheek with a cell scraper. Saliva containing BECs was processed using the same protocol as for dissociating sputum cells.

[0166] lung cancer cells

[0167] HCC15 lung cancer cells (ATCC, Manassas, VA, USA) were grown in RPMI 1640 supplemented with 10% fetal bovine serum and 1% penicillin / streptomycin in a 5% CO2 incubator set at 37°C.

[0168] Antibodies and reagents for flow cytometry analysis

[0169] Examples of antibodies that can be used to stain sputum cells include PE-labeled antibodies (anti-CD45-PE) for the pan-leukocyte surface marker CD45, anti-CD66b-FITC for identifying granulocytes, anti-CD206-FITC for identifying macrophages, anti-HLA-DR-BV421, anti-CD11b-BV650, anti-CD11b-APC, and anti-CD11c-BV650, while anti-CD3-Alexa Fluor 488 and anti-CD19-Alexa Fluor 488 can be used to label T and B lymphocytes, respectively. Anti-CD45, anti-CD11b, anti-CD3, and anti-CD19 and their respective isotype controls were purchased from BioLegend (San Diego, California, USA), while anti-CD11c, anti-CD66b, anti-CD206, anti-HLA-DR, and their respective isotype controls were purchased from BD Biosciences. Other antibodies are listed in Table 2.

[0170] Tetrakis(4-carboxyphenyl)porphyrin (TCPP) was purchased from Frontier Scientific (Logan, UT, USA), and CellMask TM Plasma membrane stain was purchased from ThermoFisher Scientific. Megabead NIST-traceable particle size standards (5, 10, 20, 30, 40, and 50 μm) were purchased from Polysciences, Inc. (Warrington, PA, USA).

[0171] All antibodies were titrated on sputum cells and, in some cases, on blood cells (CD3 and CD19) to determine the optimal staining concentration that reflects the maximum difference in fluorescence intensity compared to their isotype controls. The optimal concentrations of TCPP and EpCAM were titrated on sputum cells and HCC15 cells. Other staining reagents and microbeads were used according to the manufacturer's recommendations.

[0172] Flow cytometric analysis and cell sorting

[0173] Characterization of sputum cell populations

[0174] Now please refer to Figure 1C , cells are analyzed by flow cytometry as each cell passes through a beam from a laser and the scattering of light from the laser is detected at a forward scatter (FSC) detector and a side scatter (SSC) detector. The size and granularity of the cells can be measured as follows Figure 1E Characterization shown.

[0175] Cells in sputum samples can be separated based on the presence of live cells (LC) and dead cells (DC) and whether single cells (SC) or doublets are captured as events as described herein.

[0176] The single cell suspension samples of the isolated sputum samples in Figures 2-9 were incubated with one or more of the following probes: approximately 1 μg / ml of anti-CD45-PE, approximately 3 μg / ml of anti-CD66b-FITC, and anti-HLA-DR-BV421 (5 μg / ml), anti-CD11b-APC (4 μg / ml), anti-CD11c-BV650 (5 μg / ml), or a mixture of anti-CD3-Alexa Fluor 488 (2 μg / ml) and anti-CD19-Alexa Fluor 488 (2 μg / ml). In a separate test tube, the single cell suspension of the dissociated sputum sample was incubated with approximately 1 μg / ml of anti-CD45-PE and 4 μg / ml of anti-CD206-FITC for sputum quality determination. All incubations were performed on ice for 35 minutes in the dark. After washing with HBSS, cells were fixed with 1% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA, USA) for 30 min at 4° C. The cell suspension was then washed in cold HBSS and kept on ice until analysis.

[0177] TCPP / CyPath labeling of HCC15 spiked into sputum samples

[0178] Please refer to Figures 1-9. Dissociated sputum cells were labeled with anti-CD45 antibody and fixed as described above. HCC15 cells were harvested by trypsin, washed with DPBS (from ThermoFisher Scientific), and stained with CellMask. TM Green plasma membrane stain. Fix with 1% paraformaldehyde at 4°C and obtain the cells by CellMask TM Green-labeled HCC15 cells (cmgHCC15) were incubated for 30 minutes and washed with HBSS. Some sputum cell suspensions were spiked with 3% cmgHCC15 cells. The fixed cell mixture was then incubated with frozen TCPP (4 μg / ml) at 4°C for 1 hour. After labeling, the cells were washed and placed on ice until further analysis.

[0179] In one embodiment, samples were analyzed using a BD LSR-II flow cytometer (from BD Biosciences) equipped with four lasers (404 nm, 488 nm, 561 nm, and 633 nm). Whole sputum cells, CD45 阳性 CD206 阳性 、CD45 阳性 CD66b阳性 , or CD45 阳性 CD66 阴性 Cell Sorting of Subpopulations Post-collection data analysis was performed using FlowJo software (Tree Star, Inc., Ashland, OR, USA).

[0180] Cytological analysis

[0181] Whole sputum samples were prepared using the sputum dissociation method described above. Cytopro 7620 (Wescor, Logan, UT, USA) and Hettich 32A (Rotofix, Beverly, MA, USA) cell centrifuges were used at a rate of 1 and 2.5 × 10 cells per slide. 5 Spin slides were prepared from 100 cells. Slides were stained using either Wright or Wright-Giemsa stain according to the manufacturer's instructions. Images were generated at room temperature on a Nikon Eclipse Ti or Olympus BX40 microscope. The Nikon microscope was equipped with a UPlanApo20X / 0.7 objective lens and a DS-Ri2 camera, while the Olympus microscope was equipped with a PLAPO60X / 1.4 objective lens and an SD100 camera. Images were captured using NIS-Elements Advanced Research (Nikon) and CellSens Standard (Olympus).

[0182] Traditionally, macrophages have been used to verify the adequacy of sputum samples. The guidelines of the Papanicolaou Society for the evaluation of sputum samples by cytological analysis state that “no numerical cutoff for the number of macrophages has been reported in the literature, but an adequate specimen should have many of these readily identifiable cells” (31). HLA-DR and CD11b (or CD11c) as well as CD14 and CD206 have been shown to be useful markers for identifying different subsets of macrophages and monocytes in the lung by flow cytometry (32, 33). CD206 is a specific marker for alveolar macrophages, a long-lived cell that already populates the lung during embryonic development (34). CD206 阳性 Although macrophages are of hematopoietic origin, they are not found in the circulating blood. This macrophage population is unique to lung tissue (34) and is therefore a good candidate for verifying sample adequacy.

[0183] Preparation of sputum samples

[0184] Samples were prepared for analysis as shown in Figures 10-39. Briefly, on day 1, sputum samples were received, processed, and subjected to antibody and dye labeling. On day 2, samples were treated with TCPP and analyzed using flow cytometry. The sputum samples analyzed in Figures 10-39 were processed as described below. Samples were analyzed on a flow cytometer having at least one laser, or at least two lasers, or at least three lasers, and a plurality of channels, such as, but not limited to, five channels.

[0185] Dissociation of sputum

[0186] The sputum sample was weighed and dissociation reagent was added according to the weight as follows: 1 volume of 0.5% NAC solution was added to the sample, and 4 volumes of 0.10% DTT solution were added to the sample. The sample was vortexed and stirred at room temperature. Thereafter, 4 volumes of 1X Hank's balanced salt solution (HBSS) were added according to the current total volume (sputum + NAC + DTT solution). The sample was filtered and then centrifuged at 800x g for 10 minutes. The supernatant was aspirated and the pellet was resuspended with HBSS according to the sample amount (e.g., for small samples (≤3g), 250 microliters of HBSS were added; for medium samples (>3-≤8g), 760 microliters of HBSS were added; for large samples (>8g), 1460 microliters of HBSS were added). Cell yield was determined using a 1:10 dilution.

[0187] 0.5% N-acetyl-L-cysteine (NAC) solution: Add 0.85 g of sodium citrate dihydrate to 45 ml of ddH2O, 500 μl of 3M sodium hydroxide, and 0.25 g of NAC and stir until dissolved. Adjust the pH of the solution to approximately 7.0-8.0 and bring the volume to 50 ml with ddH2O.

[0188] 0.10% dithiothreitol (DTT) solution: Add 0.10 g of DTT to 100 ml of ddH2O and stir until dissolved. Divide the solution into 10 ml aliquots and freeze / store at -20°C until use.

[0189] Prepare a 1 mg / mL CyPath TCPP stock solution as follows: Add 25 mL of isopropanol and 0.2 g of sodium bicarbonate to 25 mL of ddH2O and stir until dissolved. If necessary, adjust the pH of the solution to between 9 and 10. Add 0.05 g of TCPP, protect the solution from light, and stir until dissolved.

[0190] Table 4 shows the microliters of cells to be aliquoted into tubes for counting and antibody labeling.

[0191] Table 4. Cell Volume (µl) to be Aliquoted into Tubes for Counting and Antibody Labeling

[0192]

[0193] *These numbers indicate the tube numbers for flow cytometry.

[0194] Antibody / FVS labeling

[0195] Sputum cells were aliquoted according to Table 4 into the reagents indicated in Table 5, which were added to form experimental and control tubes for labeling dissociated sputum cells.

[0196] Table 5: Labeling reagents

[0197]

[0198]

[0199] Tables 6, 7 and 9: Samples of bead sizes, flow cytometer compensation, isotype controls, sputum background and processed sputum prepared as described.

[0200] Table 6: Test tubes for instrument setup

[0201]

[0202] *1 drop = 60 μl

[0203] Table 7: Test tubes for sample analysis

[0204]

[0205] Incubate tubes #1-#7 in the dark for 35 minutes. Following the antibody incubation, fill each tube with cold HBSS and centrifuge the supernatant at 800 x g for 10 minutes at 4°C. Discard the supernatant and resuspend the pellet as follows: Add 0.5 ml of cold HBSS to tubes #1-#3 and store on ice at 4°C until data collection by flow cytometry. Add 2 ml of cold 1% PFA fixative to tubes #4 and #5. Add 10 ml of cold 1% PFA fixative to tubes #6 and #7. Incubate the tubes on ice for 1 hour, covered with foil. Following the fixative incubation, fill each tube with cold HBSS. Spin the cells at 1600 x g for 10 minutes at 4°C. Aspirate as much supernatant as possible without disturbing the pellet. Resuspend the pellet in the residual liquid. Resuspend tubes #4 and #5 in 0.2 ml of cold HBSS and store on ice at 4°C along with tubes #1-#3 until data collection by flow cytometry. For tubes #6 and #7, add cold HBSS according to the following formula:

[0206] Final volume of each tube (ml) = 0.15*[total number of cells / 10 6 ](Formula 1)

[0207] For cell number, obtain a cell count using trypan blue from a 1:40 dilution of the cell suspension. Add 10 microliters of the 1:40 dilution to the cell counter and count the cells in all four quadrants. An accurate cell count consists of 25-60 cells per quadrant.

[0208] Place tubes 6 and 7 on ice at 4°C overnight until ready for TCPP labeling on the following day.

[0209] Table 9: TCPP Labeling / Instrument Reagents

[0210] Reagents company HBSS Gibco 30 micron NIST beads Polysciences 20 micron NIST beads Polysciences 5 micron NIST beads Polysciences Rainbow Microbeads Spherotech

[0211] The TCPP working solution for the CyPath assay was a 20 μg / mL TCPP solution (1:50 of the stock solution) in cold HBSS and protected from light. Obtain one tube of A549 cells (test tube #8) to be used as an unstained control for FVS and TCPP labeling. Obtain one tube of A549 cells (test tube #9) to be used as a compensatory for FVS labeling. Obtain one tube of A549 cells (test tube #10) to be used as a compensatory for TCPP labeling. Obtain one tube of A549 cells (test tube #11) to be used as a compensatory for PanCK labeling.

[0212] TCPP Flags

[0213] Add the amount of TCPP working solution for Cypath analysis according to Table 10.

[0214] Table 10: TCPP labeling solution amounts

[0215]

[0216]

[0217] Incubate the sample with TCPP for approximately 1 hour. Fill tubes 6, 7, and 10 with cold HBSS and centrifuge at 1000 x g for 15 minutes at 4°C. Aspirate the supernatant without disturbing the pellet. For tubes 6, 7, and 10, wash the pellet with cold HBSS and repeat the centrifugation and washing steps. For tubes 6, 7, and 10, resuspend the pellet in the remaining liquid and add 300 μL of cold HBSS to tube 10. If the total number of cells is less than 20 × 10 6If 15 cells are present, add 250 μl of cold HBSS to tubes #6 and #7 and transfer the cells from the 15 ml conical tubes to flow cytometry tubes (labeled #6 and #7, respectively).

[0218] Flow cytometry data acquisition

[0219] It is preferred to use a flow cytometer acquisition rate below 10,000 events / second with the following settings:

[0220] Parameters used on the LSRII include threshold, FSC voltage, SSC voltage, BV510 voltage, which should be checked for all cells, including BEC, PE voltage, FITC voltage, PE-TxRed voltage, and APC voltage. For analysis optimization using equivalent flow cytometers, one of ordinary skill in the art will recognize the preferred settings for achieving the same or similar results.

[0221] Summary of fluorescence intensity values to determine the population gate:

[0222] Blood: 6 gates

[0223] Fluorophore average value scope To set up group 1 FITC 600 200-1050 PE-CF594 500 200-750 To set up group 5 FITC (border with group 6) 3300 1,000-6,000 PE-CF594 (left border) 13,000 8,000-20,000

[0224] Epithelial cells: 9 gates

[0225]

[0226] It should be noted that the settings cited are for an LSRII instrument and may differ for other flow cytometers, but it will be obvious to one of ordinary skill in the art how to compensate for different instruments to produce equivalent ranges of values.

[0227] While the above embodiments are illustrative examples of lung cancer detection, other diseases and conditions of the lungs can be detected and / or monitored over time using the systems and methods disclosed herein. For example, in cases where a subject is suspected of having or is susceptible to worsening symptoms associated with a lung disease (e.g., asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease), changes in the distribution of cell populations in sputum can be analyzed by comparison with a database of distribution profiles of control (non-disease) and diseased samples.

[0228] It should be noted that in this specification and claims, "about" or "approximately" means within twenty percent (20%) of the cited value. All computer software disclosed herein may be present on any computer-readable medium (including combinations of media), including but not limited to CD-ROM, DVD-ROM, hard drive (local or network storage device), USB smart card, other removable drive, read-only memory (ROM) and firmware.

[0229] Those skilled in the art will readily appreciate that, in at least one embodiment, the apparatus of the present invention comprises a general-purpose or special-purpose computer or distributed system programmed with computer software that implements the above-described steps, wherein the computer software may be implemented in any suitable computer language, including C++, FORTRAN, BASIC, Java, assembly language, microcode, distributed programming languages, and the like. The apparatus may also comprise a plurality of such computers / distributed systems implemented in a variety of hardware schemes (e.g., connected via the Internet and / or one or more intranets). For example, data processing may be performed by a suitably programmed microprocessor, computing cloud, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), etc., in combination with appropriate memory, network, and bus components. As the cells and particles pass through the flow cytometer, multidimensional data recorded from the analyzed cells and particles is recorded, allowing analysis and separation of cell populations based on multidimensional optical properties.

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[0277] Although the present invention has been described in detail with reference to the above embodiments, other embodiments can achieve the same results. Various changes and modifications of the present invention will be apparent to those skilled in the art, and the appended claims are intended to cover all such modifications and equivalents. The entire disclosures of all references, applications, patents, and publications cited above are incorporated herein by reference.

Claims

1. A system for detecting a likelihood that a subject has lung cancer, comprising: means for performing flow cytometric analysis on an ex vivo sputum sample labeled with a probe to obtain data comprising cell count data per cell, Wherein, the ex vivo sputum sample is labeled as follows: i） a first labeled probe that binds to a biomarker expressed on a leukocyte population of sputum cells, wherein the first labeled probe is a CD45 antibody or a fragment thereof; iv) a fourth-labeled tetrakis(4-carboxyphenyl)porphyrin TCPP probe that binds to lung cancer-associated cells in sputum samples; v） a fifth marker probe that binds to a biomarker expressed on an epithelial cell population of sputum cells, wherein the fifth marker probe is a pancytokeratin antibody or a fragment thereof; and vi) a sixth marker probe that binds to a cell surface biomarker expressed on an epithelial cell population of sputum cells, wherein the sixth marker probe is an epithelial cell adhesion molecule antibody or a fragment thereof; For determining the i） Negative for iv) and v） Sputum cells that are positive for the device identifying biomarker 2, and means for detecting the possibility that the subject has lung cancer from per-cell data based on a distribution graph of the presence or absence of a labeled probe in the per-cell labeled data, wherein the i） Negative for iv) and v） A percentage of positive sputum cells greater than 0.03% indicates that the sputum sample is positive for biomarker 2 and has a sensitivity of at least 90% and a specificity of at least 50% for detecting the likelihood of lung cancer.

2. The system of claim 1 , further comprising a method for determining a target protein based on data collected from the labeled sputum sample. i） Negative sputum cells and i） The proportion of sputum cells that are positive for identifying biomarker 1, wherein A ratio of less than 2 indicates that the sputum sample is positive for biomarker 1.

3. The system of claim 2, wherein: The biomarker 1 being positive has a sensitivity of at least 80% and a specificity of at least 50%.

4. The system of claim 2, wherein: The combination of a positive biomarker 1 and a positive biomarker 2 has a sensitivity of at least 80% and a specificity of at least 80%.

5. The system of claim 1 , further comprising a method for determining a target protein based on data collected from the labeled sputum sample. i） Negative sputum cells and v） and vi) The proportion of sputum cells that are positive for identifying biomarker 4, wherein right i） Negative for v） and vi) A percentage of positive cells greater than 2% indicates that the sample is positive for biomarker 4.

6. The system of claim 5, wherein: The biomarker 4 being positive has a sensitivity of at least 70% and a specificity of at least 70%.

7. The system of claim 1, wherein: The flow cytometric analysis included excluding cells with a diameter less than 5 microns and cells with a diameter greater than 30 microns from data analysis.

8. The system of claim 1, wherein: The flow cytometric analysis included exclusion of dead cells and cell clusters consisting of more than one cell from the data analysis.

9. The system of claim 1, wherein: The lung cancer-associated cells are tumor-associated immune cells.

10. The system of claim 1, wherein: The sputum cells are fixed or non-fixed.

11. The system of claim 1, wherein: The data includes i） and iv)-vi) The cell count data per cell of the mean fluorescence signature of any one of the marker probes is used to generate a sputum sample signature.

12. The system of claim 11, wherein: The sputum sample characteristics are indicative of lung cancer disease.

13. The system of claim 12, wherein: The sputum sample signature is compared to a database of disease-free control sputum sample signatures and lung cancer disease sample signatures to identify lung cancer disease.

14. Use of a reagent composition in preparing a flow cytometric analysis product for detecting the possibility of a subject having lung cancer, wherein the reagent composition is used to label sputum cells in an ex vivo sputum sample to perform flow cytometric analysis at a per-cell level and obtain data, wherein: The reagent composition comprises: Tetrakis(4-carboxyphenyl)porphyrin TCPP fluorescent dye; a fluorescent dye-conjugated antibody or fragment thereof that binds to the CD45 antigen; a fluorochrome-conjugated antibody or fragment thereof that binds to pancytokeratin; and a fluorescent dye-conjugated antibody or fragment thereof that binds to an epithelial cell adhesion molecule; wherein the reagent composition is used to make the ex vivo sputum sample suitable for identifying sputum cells that are negative for CD45 and positive for TCPP and pancytokeratin, Wherein, a percentage of such sputum cells greater than 0.03% indicates that the sputum sample is positive for biomarker 2, and has a sensitivity of at least 90% and a specificity of at least 50% for detecting the possibility of lung cancer.

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