SYSTEM AND METHOD FOR DETERMINING LUNG HEALTH

Abstract

Predicting the probability of lung disease in a subject, comprising labeling an ex vivo sputum sample from a subject with one or more of the following: a first labeled probe that binds to a biomarker expressed in a leukocyte 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 in a macrophage population; a fourth labeled probe that binds to a disease-related cell in the sample; a fifth labeled probe that binds to a biomarker expressed in an epithelial cell population;and a sixth labeled probe that binds to a cell surface biomarker expressed in a population of epithelial cells to obtain data comprising a mean fluorescent signature, and detect a profile based on the presence or absence of the labeled probes.

Description

Background of the invention It should be noted that the following discussion refers to several publications by author and year of publication, and that due to their recent publication dates, some publications are not considered prior art with respect to the present invention. The discussion of these publications is provided as a more complete background and is not considered an admission that these publications constitute prior art for purposes of determining patentability. Low-dose computed tomography (LDCT) is the current standard of care for lung cancer screening as an early diagnostic method, particularly in high-risk populations, defined by the U.S. Centers for Medicare and Medicaid Services (CMS) as individuals aged 55 to 75 years who smoked the equivalent of one pack of cigarettes daily for 30 years and have not quit smoking for 15 years. Although LDCT has a sensitivity of 93.8%, its specificity has been shown to be 73.4%, according to the National Lung Cancer Screening Trial (LCST), the largest lung cancer screening trial to date. The LCST showed a false-positive rate of 3%.8% for LDCT in the high-risk population studied, leading to many unnecessary, often invasive, and potentially harmful follow-up procedures in patients with a positive LDCT test but without lung cancer. Thus, there is a pressing need to improve the specificity of LDCT to reduce its false-positive rate. One strategy aimed at addressing this need is the development of additional assays with high specificity for lung cancer that can be used as an adjunct to LDCT. Highly fluorescent tetra(4-carboxyphenyl)porphyrin (TCPP) binds selectively to cancer cells compared to normal cells and is therefore uniquely suited for the development of a diagnostic marker that can distinguish cancer cells from the surrounding background cells.The standard of care for screening individuals at high risk for lung cancer is annual chest imaging using low-dose computed tomography (LDCT) (1). Although highly sensitive, LDCT has a high false-positive rate, leading to several reflex diagnostic procedures with associated risks for patients who ultimately test negative for cancer. These risks include additional exposure to high-dose radiation and complications and morbidity from invasive procedures such as thoracentesis, bronchoscopy, and hollow-core needle biopsy. The risk of adverse events and the added financial burden associated with these procedures are significant, resulting in a clear medical need for safer and less invasive reflex testing following positive LDCT results (2).Alternative testing methods would ideally complement the high sensitivity of the LDCT by increasing specificity, reducing the false positive rate, and improving the positive predictive value of selection with an ancillary test at a reasonable cost. Minimally invasive techniques in the form of liquid biopsies have been proposed for reflex testing for lung cancer following positive LDCT results. Using a liquid biopsy, circulating tumor cells (CTCs) and tumor-free nucleic acids can be collected from a peripheral blood sample of the patient. The CTCs and nucleic acids are then tested using molecular techniques, such as next-generation sequencing (NGS), to detect the presence of cancer-associated gene mutations, which could predict the presence of cancer and how the patient's tumor might respond to targeted therapy (3). Although these technologies can identify mutations in an estimated 50–75% of lung cancers (4, 5), LDCT-positive patients whose tumors lack these specific gene abnormalities will have negative results from a liquid biopsy.Furthermore, CTCs are rare (barely 1 cell per 10⁹ normal cells), and tumor nucleic acid concentrations are often below the detection limit of most clinically available molecular testing methods (6). Thus, liquid biopsies have the potential to provide valuable treatment information about the patient's tumor genome, but they are best used at a later stage of the lung cancer diagnostic algorithm, after tests aimed at earlier cancer diagnosis. Liquid cytology testing of bronchial washings provides sampling of potentially malignant cells for pathology review using conventional sputum smears. Bronchoscopy procedures used to retrieve cells from a patient's airways are less invasive than a hollow-core needle lung tissue biopsy. However, the risk of adverse events such as hemorrhage still exists (7). In addition, the associated healthcare costs, particularly if performed on a hospitalized patient, can be significant. Given that only a small minority (i.e., less than 4%) of LDCT-positive patients will actually be found to have lung cancer, the medical need for alternative, cost-effective, and more accessible sources of malignant lung cells to provide diagnostic material persists. Pathologists have performed routine sputum cytology for decades as a non-invasive, rapid, and specific screening method for lung cancer. In conventional sputum cytology, samples are stained, and malignant cells are selected microscopically. However, conventional sputum cytology has the disadvantage of low sensitivity (~65%) (8). Several methods have been tested to increase the sensitivity of sputum analysis, including KRAS mutation testing. Although the KRAS test can be both sensitive and specific if a patient's tumor does indeed harbor a mutated KRAS gene, only 15–20% of lung cancers actually harbor KRAS mutations. Thus, tumor cells that are negative for the KRAS mutation will not be detected by this technique (9).An alternative DNA-based strategy, called automated sputum cytometry, uses special staining and computer-assisted image analysis to evaluate the nuclear DNA characteristics of sputum epithelial cells to detect changes associated with malignancy. Although this technique is slightly more sensitive than conventional cytology, its specificity is only ~50% (10). Brief description of the invention One embodiment of the present invention provides a method for predicting the probability of lung disease in a subject. The method comprises the steps of labeling an ex vivo sputum sample with one or more of the following: i) a first labeled probe that binds to a biomarker expressed in 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 in a granulocyte population of sputum cells, a T cell probe that binds to a biomarker expressed in a T cell population of sputum cells, a B cell probe that binds to a biomarker expressed in a B cell population of sputum cells,or any combination thereof; iii) a third labeled probe that binds to a biomarker in a macrophage population; iv) a fourth labeled probe that binds to a disease-related cell in the sputum sample; v) a fifth labeled probe that binds to a biomarker expressed in an epithelial cell population of the sputum cells; and vi) a sixth labeled probe that binds to a cell surface biomarker expressed in an epithelial cell population of the sputum cells. The labeled sputum sample is analyzed, for example, by flow cytometry analysis, to obtain data comprising cell-level cytometric data,Based on the mean fluorescent signature of any of the labeled probes (i)–vi), per-cell data are detected to determine the probability of lung disease in a subject based on a profile of the presence or absence of the labeled probes in the per-labeled cell data. The data obtained can be further analyzed to identify the presence or absence of a biomarker in a sputum sample. For example, disease-related cells may be lung cancer cells or tumor-associated immune cells. The lung disease may be one selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer. In addition, the sputum cells that are labeled may be fixed or unfixed. Data collected from the labeled sputum sample can be characterized by the cell populations and biomarkers identified within them. For example, a proportion of sputum cells in the data collected from the labeled sputum sample are determined to be negative for i), compared to sputum cells that are positive for i), to identify a biomarker 1. In one example, a proportion less than 2 indicates that the sputum sample is positive for biomarker 1. In one modality, the positive biomarker 1 has a sensitivity of at least approximately 80% and a specificity of at least 50% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample, with the application of biomarker 1. Where 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%. In another example, from the data collected from the labeled sputum sample, identifying the sputum cells that are negative for i) and positive for iv) and v) to identify a biomarker 2. For example, a percentage of sputum cells negative for i) and positive for iv) and v) that is greater than 0.03% indicates that the sputum sample is positive for biomarker 2. In one modality, the positive biomarker 2 has a sensitivity of at least 90% and a specificity of at least 50% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample, with the application of biomarker 2. Where 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%. In another example, biomarker 3 is identified when sputum cells are positive for i) and iii) and exhibit FITC autofluorescence. For example, a percentage of sputum cells positive for i) and iii) and exhibiting FITC autofluorescence greater than 0.03% indicates that the sputum sample is positive for biomarker 3. In one modality, a positive biomarker 3 has a sensitivity of at least 60% and a specificity of at least 70% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample when biomarker 3 is applied. 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%. In another example, biomarker 4 is identified when sputum cells are negative for i) and positive for v) and vi). For example, a percentage of cells negative for i) and positive for v) and vi) of more than 2% indicates that the sample is positive for biomarker 4. In one modality, a positive biomarker 4 has a sensitivity of at least 70% and a specificity of at least 70% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample, with the application of biomarker 3. Where the sensitivity is at least 80%, 85%, 90%, or 95%, and the specificity is at least 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In another modality, more than one biomarker can be combined, such as a combination of positive biomarker 1 and positive biomarker 2, to produce a sensitivity of at least 80% and a specificity of at least 80% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample, with the application of biomarkers 1 and 2. Furthermore, the combination of positive biomarkers 1, 2, and 3 produces a sensitivity of at least 80% and a specificity of at least 80% for distinguishing a sputum sample with lung cancer (C) from a high-risk (HR) sputum sample, with the application of biomarkers 1 through 3. Moreover, positive biomarkers 1 through 4 produce a sensitivity of at least 70% and a specificity of at least 75% for distinguishing a sputum sample with cancer. lung (C) from a high-risk (HR) sputum sample, with the application of biomarkers 1 to 4.Where 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%. In one modality, flow cytometry analysis may include one or more of the following: excluding from data analysis those cells that have a diameter less than approximately 5 pm and greater than approximately 30 pm, those cells that are dead cells, and cell clusters of more than one. In another modality, the first labeled probe that binds to a biomarker expressed in a population of sputum cells can be a CD45 antibody or fragment thereof. In another modality, the second labeled probe is one or more of the following, added individually or in combination to the sputum sample: the granulocyte probe that binds to a biomarker expressed in a granulocyte population of sputum cells and can be selected from a CD66b antibody or fragment thereof, the T cell probe that binds to a biomarker expressed in a T cell population of sputum cells is a CD3 antibody or fragment thereof, the B cell probe that binds to a biomarker expressed in a B cell population of sputum cells is a CD19 antibody or fragment thereof. In another modality, the third labeled probe that binds to a biomarker in a population of sputum cell macrophages is an antibody to CD206 or a fragment thereof. In yet another modality, the fourth labeled probe that binds to a disease-related cell in the sputum sample is a tetra(4-carboxyphenyl)porphine (TCPP). In yet another modality, the fifth labeled probe that binds to a biomarker expressed in a population of epithelial cells of sputum cells is a panCytokeratin antibody or fragment thereof. In an additional modality, the sixth labeled probe that binds to a cell surface biomarker expressed in a population of epithelial cells from sputum cells is an EpCam antibody or fragment thereof. The collected data may comprise cell-by-cell cytometry data based on a mean fluorescent signature of any of the labeled probes i) to vi) to produce a sputum sample signature. The sputum sample signature identifies lung health or lung disease. Lung disease may be selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer. In addition, the sputum sample signature is compared to a database of control (non-disease) sputum sample signatures and lung disease sample signatures to identify lung disease. In some embodiments of the present invention, the results are classified using a trained algorithm.The trained algorithms of the present invention include algorithms that have been developed using a series of known reference sputum samples from a subject at high risk of developing the disease, sputum samples from subjects with confirmed disease, and sputum samples from subjects identified as normal (who do not have the disease or are not at high risk of developing the disease). Suitable algorithms for classifying the samples include, without limitation, k-th 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 one embodiment of the present invention may incorporate data other than sputum sample signatures or cell cytometry data or mean fluorescent signature, such as diagnoses made by cytologists or pathologists, or information about the subject's medical history. On a programmed computer, the data are fed into a trained algorithm to generate a classification of the sputum sample as high, intermediate, or low probability of having the lung disease, and to electronically produce a report identifying that classification for the lung disease. One embodiment of the present invention provides a first reagent composition for flow cytometry phenotyping of sputum cells from a subject's sputum sample, to identify one or more biomarkers within the cell population that are associated with a probability of lung cancer, wherein the reagent composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome; and antibodies conjugated to the fluorochrome directed against selected cell markers of ii) EpCAM, and / or pancytokeratin, and ii) CD45, CD206, CD3, CD19, CD66b or any combination thereof. Another embodiment of the present invention provides a second reactive composition for flow cytometry phenotyping of sputum cells from a subject's sputum sample, to identify one or more biomarkers within the cell population that are associated with a probability of lung cancer, wherein the reactive composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome and antibodies conjugated to the fluorochrome directed against the cell markers: ii) EpCAM, and / or panCytokeratin, and iii) CD45. Another embodiment of the present invention provides a third reactive composition for flow cytometry phenotyping of sputum cells from a subject's sputum sample, to identify one or more biomarkers within the cell population that are associated with a probability of lung cancer, wherein the reactive composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome; and antibodies conjugated to the fluorochrome directed against one or more of the following cell markers: CD45, CD206, CD3, CD19, and CD66b. nzRnnn / Lznz / q / Yi Another modality provides a method for predicting the probability of lung disease in a subject, comprising the steps of labeling an ex vivo sputum sample with i) a labeled probe that binds to a disease-related cell in the sputum sample, and ii) one or more fluorochrome-conjugated probes directed against a sputum cell marker. The labeled sputum sample is analyzed by flow cytometry to obtain data comprising per-cell cytometric data based on the mean fluorescent signature of any of the labeled probes i) to ii). From the per-cell data, the probability of lung disease in a subject is detected based on a profile of the presence or absence of i) and ii) in the labeled per-cell data. The per-cell cytometric data can be based on the mean fluorescent signature of any of i) to ii) to produce a sputum sample signature.In one modality, the sputum sample signature identifies the lung disease; for example, the lung disease is selected from a group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer. Furthermore, the sputum sample signature is compared to a database of signatures from control (non-disease) sputum samples and signatures from lung disease samples to identify the lung disease in the labeled sputum sample. In one modality, the labeled probe that binds to a disease-related cell in the sputum sample is tetra(4-carboxyphenyl)porphine (TCPP). The further scope of applicability of the present invention will be established partly in the detailed description that follows, taken in conjunction with the accompanying drawings, and partly will be evident to those skilled in the art upon examination of the following, or may be learned through the practice of the invention. The objects and advantages of the invention can be realized and achieved by means of the instruments and combinations specifically mentioned in the appended claims. Brief description of the figures The accompanying drawings, which are incorporated in and form part of the descriptive text, illustrate one or more embodiments of the present invention and, together with the description, serve to explain the principles of the invention. The drawings are solely for the purpose of illustrating one or more embodiments of the invention and should not be construed as limiting the invention. In the drawings: Figures 1A to 1B illustrate cytocentrifuge slides of dissociated sputum cells. Wright-Giemsa stained cytocentrifuge slides of sputum cells processed before staining with antibodies or TCPP. Figures 1C to 1E illustrate a flow cytometry-based system having a light source and detector for analyzing the optical properties of a cell or particle, with front scattering (FSC) and side scattering (SSC) identified as exemplary optical properties of the cell or particle passing through the laser light source zone over time, with pulse height and area measurements as measurements in the histogram shown in Figure 1D. Figures 2A to 2I illustrate flow cytometry dot diagrams, Figures 2A to 2F and contour diagrams Figures 2G to 2I of the beads (Figure 2A and Figure 2G) and cells (Figures 2B to 2F, Figure 2H, and Figure 21). Figures 3A to 3K illustrate dot plots and contour diagrams for the identification and characterization of hematopoietic cells in sputum. Figures 4A to 4G illustrate dot plots (Figure 4A, Figure 4C, Figures 4F to 4G) and histograms (Figure 4B, Figure 4D and Figure 4E) of CD45P°positive sputum cells exposed to the CD66b probe or the CD206 probe. Figure 5 is a graph that illustrates on the Y axis the number of macrophages / slide shown as solid circles with an “x” inside and the CD45P°si,ivas / CD206P°sitivas cells shown as solid circles, and on the X axis the sample number, where the presence of the CD206P°si,ivas cell population coincides with the presence of many macrophages in a sputum smear. Figure 6 illustrates a flow diagram of the sputum sample preparation for analysis. HCC15 cancer cells were labeled with CellMask™ Green (step 1), while, in a separate tube, dissociated sputum cells were stained with an PE-labeled anti-CD45 antibody (step 2). Figures 7A to 7F illustrate dot plots of sputum cells, Figure 7A represents the CD45 frame, Figure 7B represents a TCPP frame in CD45P cells, Figure 7C represents the TCPP frame in CD45negative cells, and Figures 7D to 7F represent unstained sputum cells treated with isotype control and stained sputum cells. Figures 8A to 8B illustrate a preliminary comparative analysis of sputum samples obtained from healthy volunteers and high-risk patients with and without lung cancer. Five sputum samples from different donors were analyzed similarly to the experiment detailed in Figure 6 and Figures 7A to 7F. The white dots represent a sample from a healthy volunteer (H), the black dots represent a sample from a high-risk patient without cancer (HR), and the dot marked with an x ​​represents a sample from a patient with confirmed lung cancer (C). Figure 8A illustrates the total number of CD45-negative (left) and CD45-positive (right) cells in each sample analyzed. Figure 8B illustrates the proportion of positive-tissue proton pump inhibitors (PTPPIs) to CD45-negative (left) and CD45-positive (right) cells in each sample analyzed. Figures 9A to 9F illustrate dot diagrams of a strategy for analyzing sputum cells to detect the presence of TCPPP°si cells, according to an embodiment of the present invention. Figures 10A to 10B illustrate the QC beads and sputum sample tube #6 described in the protocol, analyzed by flow cytometry, and the resulting scatter plots. Figure 10A illustrates the bead size exclusion (BSE) frame that is first established in the profile obtained by running the QC beads. Figure 10B illustrates the BSE frame applied to all sputum samples. Figures 11A to 11F illustrate sputum samples being analyzed by flow cytometry and the resulting dot plots for the determination of unstained sputum cells (tube #4) as illustrated in Figure 11A, Figure 11B and stained sputum cells (tube #6) as in Figure 11C to identify live cells (LC) as illustrated in the box in Figure 11C and single cells (SC) as illustrated in Figure 11D. Figure 11E and Figure 11F illustrate sputum cell dot plots to establish the isotype control of Figure 11E and the CD45-positive and CD45-negative cell populations remaining after the application of the BSE, LC, and SC frames. Figures 12A to 12C illustrate the analysis of CD45P+ cells from a sputum sample from tube #6. All profiles represent CD45P+ cells that have been selected using the BSE, LC, and SC frames. Figures 13A to 13B illustrate isotype checkpoint diagrams for FITC / Alexa488 (F / A) (tube #5) and probe-treated cells for the cell marker CD66b / CD3 / CD19 conjugated to (F / A) (tube #6). Figures 14A to 14B illustrate dot plots of the PE-CF594 isotype control (tube #5) and probe-treated cells for the cell marker CD206 conjugated to PE-CF594. Figures 15A to 15B illustrate a dot plot of isotype control for FITC / Alexa488 on the Y-axis and PE-CF594 on the X-axis of sputum cells (tube #5). A double-negative frame or population parameter of 1 is established. A dot plot in Figure 15A and a pseudocolor plot in Figure 15B of the isotype control are presented, framed by BSE, LC, and CD45P-positive cells. The horizontal dashed line represents the FITC / Alexa488 positive / negative boundary determined in Figures 13A to 13B, while the vertical dashed line is derived from the PE-CF594 positive / negative boundary determined in Figures 14A to 14B. Figures 16A to 16B illustrate a dot plot (Figure 16A) and a pseudocolor graph (Figure 16B) of a sputum sample from tube #6 and measurements for the mean fluorescence intensity of an antibody cocktail (CD66b / CD3 / CD19-FITC / Alexa488) (Y-axis) and the CD206 marker conjugated to PE-CF594 (X-axis). CD45-positive cells, also selected using BSE, LC, and SC frames, are shown. The same population 1 (solid inner box) and boundaries (dotted lines) drawn in Figures 15A to 15B apply to these profiles. Figures 17A to 17C illustrate pseudocolor diagrams generated from the CD45-positive sputum tube of two samples (Figure 17A and Figure 17B are the same) and apply the frames established for populations 2 to 6 of the sputum sample in Figures 16A to 16B. All diagrams show CD45-positive sputum cells that have been framed using the BSE, LC, and SC frames. The dashed horizontal and vertical lines were established on the isotype controls (not shown). Figures 17A to 17B show, in a drawing of frames 4 and 5, when the mean FITC fluorescence intensity of population 5 is intermediate and crosses the horizontal boundary line. Figure 17C illustrates an upper right frame of population 6. Figure 18 illustrates a graph of the percentage (%) of all blood cells (CD45-positive) in a sputum sample on the Y-axis, and the profile type 1, 2, and 3 on the X-axis. The illustrated signature is for profile 1 for CD45-positive cells for high-risk (HR) samples. Figures 19A to 19C illustrate graphs of signatures 1 to 3 for CD45positive sputum cells from HR and cancer cells and analysis of population 6 as a percentage of all CD45positive blood cells for HR and C sputum sample. nzAnnn / Lznz / q / Yi Figures 20A to 20D illustrate dot plots of CD45ne9a sputum samples, with frames drawn for the different epithelial subpopulations of the sputum. Figures 21A to 21B illustrate a type checkpoint diagram for FITC / Alexa488 and CD45-positive sputum cells (tube #5) and panCytokeratin / Alexa488-labeled sputum cells (tube #7). The threshold for positive FITC / Alexa488 staining in CD45-negative sputum cells is determined. Figures 22A to 22B illustrate an isotype checkpoint diagram for PE-CF594 and sputum cells (tube #5) and EpCAM-PE-CF594-labeled sputum cells (tube #7). The cutoff for PE-CF594 positive staining in CD45ne9a sputum cells and sputum is determined. Figures 23A to 23B illustrate dot plots of CD45negative cells with isotype controls (tube #5) framed by BSE, LC, and CD45 cell frames. The horizontal dashed line represents the FITC / Alexa488 positive / negative boundary determined in Figures 21A to 21B, while the vertical dashed line is derived from the PE-CF594 positive / negative boundary determined in Figures 22A to 22B. Figures 24A to 24B illustrate sputum cell dot diagrams and frames for populations 2 to 9 of CD45-negative cells. Figure 25 illustrates a separate graph of the CD45ne9ativas dot diagrams for profiles 1 to 4 with different signatures for populations 1 to 9. Figure 26 illustrates a signature for profile 1 through the median of population 1, population 2, population 5 and panCK++. Figure 11 illustrates a comparison of signatures 1 to 4 for CD45-negative cells from a sputum sample of subjects classified as being at high risk of developing lung cancer and sputum samples from subjects classified as having lung cancer. Figures 28A to 28B illustrate a sensitivity of 80% and a specificity of 85% for the application of the biomarker, resulting from the amount of PanCK++ (populations 3+4+9) as a percentage (%) of all CD45re9a cells in a sputum sample. Figures 29A to 29C illustrate a cancer risk analysis of cells in an HR sputum sample and C sputum samples to determine the proportion of CD45ne9ati' / o / CD45P°sitivo (biomarker 1) of cells in the sputum sample. Figures 30A to 30B illustrate a specificity of 90% and sensitivity of 54% for identifying samples from a patient with lung cancer or a subject at high risk of developing lung cancer by applying biomarker 1 to the analyzed sputum sample. Figures 31A to 31C illustrate a cancer risk analysis of CD45negative cells in a sputum sample (tube #7) positively labeled with TCPP (biomarker 2). Figures 32A to 32B illustrate a specificity of 63% and sensitivity of 100% for identifying samples from a patient with lung cancer or a subject at high risk of developing lung cancer by applying biomarker 2 to the analyzed sputum sample. Figures 33A to 33C illustrate a combination of biomarker 1 and biomarker 2 identified in Figure 25 and Figure 27 for analyzing a sputum sample for HR and C nzAnnn / Lznz / q / Yi sputum samples to produce 90% sensitivity and 90% specificity according to an embodiment of the present invention, for the identification of samples from a lung cancer patient or a subject at high risk of developing lung cancer with the application of biomarker 1+2 to the analyzed sputum sample. Figures 34A to 34C illustrate dot plots of CD45P cells, used to identify the number of cells in population 6 (biomarker 3) of HR and C sputum samples as the % of all CD45+ cells in the sample. Figures 35A to 35B illustrate a specificity of 88% and sensitivity of 60% for identifying samples from a patient with lung cancer or a subject at high risk of developing lung cancer by applying biomarker 3 to the analyzed sputum sample. Figures 36A to 36B illustrate a cancer risk analysis of CD45ne9a cells from a sputum sample that were also found to be panCytokeratinPositive (biomarker 4) in populations 3+4 and 9 of HR and C sputum samples. Figures 37A to 37B illustrate a specificity of 83% and sensitivity of 80% for identifying samples from a patient with lung cancer or a subject at high risk of developing lung cancer by applying biomarker 4 to the analyzed sputum sample. Figures 38A to 38E illustrate a cancer risk analysis of a sputum sample cell by applying biomarkers 1 to 4 to HR and C sputum samples with 98% specificity and 78% sensitivity. Figure 39 illustrates a flowchart for lung health selection of subjects, including a system and method for fractionating lung cell populations as described herein, and an algorithm for classifying sputum sample as high risk, intermediate risk, and low risk for lung disease. Detailed description of the invention Furthermore, the following terms will have the definitions set forth below. It is understood that, in the event a specific term is not defined herein, that term will have the meaning within its typical usage in the context of technical experts in the field. It should be noted that, as used herein and in the appended claims, the singular forms a / an, and the / the include plural references unless the context clearly indicates otherwise. The term calibrate means to establish the sensitivity of the machine to the control reagents. The term offset means that the samples are compared against the controls to determine the background. The term fractionalizing means selecting a subset of events for further analysis. An example of fractionalizing is using "frames" to exclude or include data during analysis. nzAnnn / Lznz / q / Yi The term “framing” means that boundaries are placed around cell populations with common characteristics, usually frontal scattering, lateral scattering, and marker expression, in order to investigate and quantify these populations of interest. The term “probe” means a ligand, peptide, antibody, or fragment thereof that has an affinity for a biomarker and binds to it on the surface of a cell or particle, or to a marker inside the cell or particle. Porphyrins are concentrated in all types of cancer cells. In addition, certain porphyrins are naturally fluorescent, with a characteristic photon emission profile. A porphyrin composition is described here for use in a high-throughput assay (specifically a flow cytometry assay) to distinguish the fluorescence of porphyrins that mark cancer cells or cells associated with a disease from the surrounding background cells (11). Now, referring to Figures 1A and 1B, cytocentrifuge slides of dissociated sputum cells are illustrated. Wright-Giemsa-stained cytocentrifuge slides of sputum cells processed before antibody or TCPP staining are illustrated in Figure 1A. Figure 1A contains too many buccal epithelial cells (BECs) (some of which are indicated with an asterisk). Macrophages are indicated with an arrow, and debris with an arrowhead. Figure 1B shows the presence of less debris (indicated with arrowheads), which allows for easier identification of BECs and macrophages on the slide. In flow cytometry, each cell or particle is hydrodynamically focused onto a photocell. Each cell or particle passes through one or more beams of light as it passes through the photocell. The light scattering or fluorescence (FL) emission (if the cell or particle is labeled with a fluorophore) provides information about the cell's / particle's properties. Lasers are the most commonly used light sources in modern flow cytometry. Lasers produce a single wavelength of light (a laser line) at different frequencies (coherent light). They are available at different wavelengths, ranging from ultraviolet to far-red, and have a variable range of energy levels (photon output / time). The light scattered forward, typically at an angle of up to 20° to the laser beam axis, is collected by a photomultiplier tube (PMT) or photodiode known as a front-scatter channel (FSC).The FSC is approximately equal to the size of the cell / particle. Typically, larger cells refract more light than smaller cells. Light measured at an angle of approximately 90° to the excitation line is called side scatter (SSC). The SSC channel provides information about the relative complexity (e.g., granularity and internal structures) of a cell or particle. Both FSC and SSC are unique to each cell or particle, and a combination of the two can be used to roughly differentiate cell types in a heterogeneous sample such as, but not limited to, blood or sputum. An event is identified when a cell or particle passes through the laser beam, and a time-dependent signal is generated.For FSC and SSC, the time the cell or particle remains in the laser is measured as the event width “W”, while the maximum height of the current output measured by the photomultiplier tube is the height “H”, and the area “A” represents the integral of the impulse generated by the cell or particle passing through the laser beam interrogation point in the cytometer. As used herein, each cell and particle can be recorded as an event when it passes through the light beam in the photocell. Now, referring to Figure 1C, a light scattering profile is illustrated (where front-side scattering (FSC) represents cell size and side-side scattering (SSC) represents granularity), where “A” represents the integral of the impulse generated by the cell or particle passing through the query point of a cytometer. Figure 1D is a histogram resulting from the laser pulse intensity (H) on the Y-axis and time (W) on the X-axis, with the area under the curve indicated as (A). Figure 1E illustrates a plot of SSC-A vs. FSC-A for cells with different granularity and size. A light scattering profile (where front-side scattering (FSC) represents cell size and side-side scattering (SSC) represents granularity), where “A” represents the integral of the impulse generated by the cell or particle passing through the query point of a cytometer. Light scattering frameworks to enrich RFC The epithelial and specialized cells of the airways, as well as the glandular cells lining the bronchi, secrete mucus. Mucus produced deep within the lung can contain a wide variety of cells recycled from lung tissue, including epithelial cells, alveolar cells, macrophages, and other hematopoietic (blood) cells (17). Mucus also contains noncellular material, which is especially prominent in the lungs of people who smoke, live in heavily polluted areas, or are exposed to other airway allergens (such as pollen). When mucus originating within the lungs is expectorated, it is called sputum. Sputum is frequently mixed with saliva produced in the oral cavity, which contains many BECs (or cheek cells), adding another cellular component to an already complex tissue sample (see Figures 1A–1E). Unlike microscopy, flow cytometry can provide multidimensional or more precise information about sputum cell populations because it allows the removal of debris and cells of no interest based on size, granularity, or fluorescence markers, thus enriching the sample with the cells of interest. To enrich red fluorescent cells (RFCs) in sputum cell analysis, the first step is to approximate the size (diameter) of the RFCs; anything smaller or larger is excluded. RFCs are the cells with the highest TCPP uptake—that is, cancer cells and cancer-associated macrophages—because both cell types take up more TCPP than any other cell type. The size of lung cancer cells can vary and depends on the type of cancer, but it is unlikely to differ significantly from that of cultured lung cancer cells.A literature search (Table 1) reveals that the diameter of HCC15 lung cancer cells is 20–30 µm, for example, while the measured diameter of alveolar macrophages is 21 µm. Macrophages and lymphocytes are of particular interest, since specific subpopulations of each of these cell types are known to alter their function when associated with cancers (23–26). However, RBCs (6–8 µm) and anything smaller (debris), as well as BECs (65 µm) and anything larger, can be excluded from further analysis. nzRnnn / Lznz / q / Yi Table 1. Cell type Diameter (pm) Reference Hematopoietic cells Erythrocytes 6 to 8 Wheater et al. (43) Granulocytes 9 to 12 Wheater et al. (43) Monocytes 14 to 17 Wheater et al. (43) Lymphocytes 7 to 8 Wheater et al. (43) Other Alveolar macrophages 21 Krombach et al. (44) Type I alveolar epithelial cell (lung cells) up to 50 Kini (45) Type II alveolar epithelial cells (lung cells) 9 to 15 Kini (45) Buccal epithelial cells (cheek cells) 65 Paszkiewicz et al. (14) HCC15 lung cancer cells 20 to 30 Fillmore et al. (46) nzRnnn / Lznz / q / Yi Referring to Figures 2A to 2I, flow cytometry profiles are shown illustrating cells with SSC and FSC signatures. Flow cytometry scatter plots are shown in Figures 2A to 2F, and contour plots in Figures 2G to 2I, of beads (Figure 2A and Figure 2G) and cells (Figures 2B to 2F, Figure 2H, and Figure 2I). Figure 2A is a light scatter plot showing, from left to right, beads of 5, 10, 20, 30, and 50 pm. The size of individual beads is manually plotted on the horizontal FSC axis and transferred to Figures 2B to 2F. The SSC was initially kept low so that cells with a higher-than-expected SSC could be visualized. Figure 2B is a light scattering diagram of erythrocytes (RBCs) stained with CellMask™ Orange. Figure 2C is a light scattering diagram of leukocytes (WBCs) stained with CellMask™ Far Red.Figure 2D is a light scattering diagram of squamous cell lung carcinoma (HCC15) cells stained with CellMask™ Orange. Figure 2E is a light scattering diagram of buccal epithelial cells (BECs) stained with CellMask™ Green. Figure 2F is a light scattering profile of white blood cells (WBCs) (positioned as in Figure 2C), HCC15 cells (positioned as in Figure 2D), and BECs (positioned as in Figure 2E) placed together in a test tube. The striped box in Figure 2F indicates the light scattering frame that includes the cells of interest; it includes everything from 5 to 30 µm in size. Figure 2G represents 5 µm (bottom) and 30 µm (top) beads on an FSCW x SSCW light scattering contour plot. Figure 2H is an FSC-W x SSC-W light scattering contour diagram of BECs stained with CellMask™ Green (as seen in Figure 2E).Figure 21 illustrates the combined cell populations (WBC, BEC, and HCC15) presented in an FSC-W x SSC-W light scattering contour plot. The separation between BECs (cells larger than 30 pm and located outside the dashed box) and the cells of interest (cells smaller than 30 pm located inside the dashed box) is clearly visible. The dashed box indicates the W x W frame and identifies the population of interest, allowing for easy exclusion of most BECs. In one modality, debris and BECs are excluded from a cell population for further analysis. Standard-sized beads (5, 10, 20, and 50 µm) are used in a light scattering profile (where front scattering (FSC) represents cell size and side scattering (SSC) represents granularity; Figure 2A). To confirm whether these beads can actually predict cell sizes according to the information presented in Table 1, the beads are compared with RBCs, WBCs, and BECs isolated from healthy volunteers, as well as HCC15 lung cancer cells. The different cell types are labeled with CellMask™ dyes of different colors so that they can be analyzed separately (2B to 2E) and in combination (Figure 2F). As illustrated in Figure 2B and predicted in the literature (Table 1), RBCs correspond to the smallest beads.Similarly, WBCs range approximately from 10 to 20 µm in size (Figure 2C), while most HCC15 cells are less than 30 µm in diameter (Figure 2D). When saliva was analyzed by flow cytometry (which consists mainly of BECs), contrary to expectations reported in the literature, most BECs projected as cells of 30 µm or less (Figure 2E) and not as cells larger than 50 µm. These results demonstrate that size can be used to exclude debris (by removing anything smaller than or equal to 5 µm beads), but size cannot be used to exclude BECs. BECs exhibit very high SSC characteristics that distinguish them from WBCs and HCC15 cells (Figure 2F). SSC and FSC are translated by the flow cytometer as electronic signals with values ​​of height (H), width (W), and area under the curve (A). When considering the various combinations of SSC and FSC parameters, the SSC-W and FSC-W profiles resulted in a profile that allowed the elimination of most BECs by establishing a frame around the cells that exhibit a lower SSCW than the 30 µm beads (Figures 2G to 21). Subfractionation of hematopoietic cells in distinct populations Another aspect of sputum analysis by flow cytometry is the characterization of the various hematopoietic (blood) cell populations. The common WBC marker, CD45, is expressed on the cell surface of all WBCs. Using a probe, for example, an antibody, directed against the CD45 antigen, hematopoietic cells (CD45-positive cells) can be distinguished from other cells, including normal lung epithelial cells and potential lung cancer cells (CD45ne9a, IV3B cells). To identify specific hematopoietic subpopulations in sputum, the present inventors used additional probes, for example, antibodies directed against granulocytes (CD66b), macrophages (HLA-DR, CD11b, CD11c, CD206), and lymphocytes (CD3 and CD19). Table 2 identifies exemplary probes and fluorophores. nzRnnn / Lznz / q / Yi Table 2. Antibody Marker / Fluorophore Dye Laser Source: Tube #1 Tube #2 Dead Cells Viability Staining BV510 4IJ5 n>T and Cancer Cells TCPP APC and (associated) Leukocytes CD45 PE 561 rtm Granulocytes CD66b FITC — Γ Cells CD3 Alexa488 488 nm > / B Cells CD19 Alexa488 Macrophages CD206 PE-CF594 (Tx-Red) 561 nm Epithelial Cells EpCAM PE-CF594 (Tx-Red) z Cytokeratin (CK) FITC 488 ran nzAnnn / Lznz / q / Yi Now, referring to Figures 3A to 3K, the identification and characterization of hematopoietic cells in sputum is illustrated. Figure 3A illustrates sputum cells presented on an FCS-A vs. SSC-A light scattering plot. The black balls with numbers on the x-axis represent the size of the beads used to establish this light scattering frame, which excludes debris and BECs—that is, anything smaller than the 5 µm beads (left vertical line) and anything larger than 30 µm (right vertical line). Figure 3B illustrates an FSC-W x SSCW contour plot of the cells within the light scattering frame of Figure 3A (where “W” represents the signal width). The 30 µm size exclusion frame is identified as the horizontal line, such that any cell detected in the upper box is larger than 30 µm.Figure 3C shows a dot plot of FSC-A vs FSC-H cells selected by the W x W frame shown in Figure 3B, where W represents the maximum current output by the photomultiplier tube detecting the cytometer's laser light. The rectangle indicated in the frame includes all single cells, while cell doublets are excluded. Figure 3D illustrates a dot plot of sputum cells preselected from the light-scattering frames shown in Figures 3A to 3C, stained with the PE isotype control to determine the frame for CD45 specificity (indicated by the top box). Figure 3E illustrates a dot plot of sputum cells preselected from the light-scattering frames shown in Figures 3A to 3C, where the cells are stained with an anti-CD45-PE antibody.All cells expressing the CD45 antigen (CD45-positive cells) are captured in the upper box. The cells in the upper box of the CD45-positive frame are then further analyzed for CD66b expression. The background fluorescence of the anti-CD66 antibody is shown in Figure 3F, based on staining with a FITC isotype control. Figure 3G shows CD45-positive cells stained with anti-CD66b. CD45-positive / CD45-negative cells are indicated by the upper box. Figure 3H shows the Wright-Giemsa stain of cells sorted from the upper box in Figure 3B. Figure 31 illustrates a dot plot showing unstained sputum cells selected using only the BSE frame. This particular sample contains a large subpopulation of cells within the box showing intermediate staining in the PE channel, the channel used to detect CD45 expression.The presence of this subpopulation makes it difficult to determine where to draw the line to separate the sample into CD45-negative and CD45-positive cells. Figure 3J illustrates a dot plot showing a WxW frame of the same sample as in Figure 31. The cells in the lower box (the WxW frame) are the cells of interest, while the cells captured in the upper box are SECs, which must be excluded to reveal the true unstained sputum population of interest. Figure 3K illustrates unstained sputum cells selected using the BSE frame and the WxW frame: the negative population is clearly identifiable, and the QQ45-negative frame has a medium fluorescence intensity that is within the "frame" of the horizontal line. Figures 3A to 3K illustrate a representative sample obtained from a patient at high risk of developing lung cancer. The first two profiles in the top panel (Figure 3A and Figure 3B) show light-scattering frames to exclude debris and BECs, respectively. An additional doublet discrimination frame was also applied to exclude cell doublets (Figure 3C). Cells within the diagonal box are single cells (SCs). The top right profile (Figure 3D) shows the cells selected by the three light-scattering frames above (which remove debris, BECs, and cell doublets), stained with a PE-labeled isotype control antibody to determine the background staining of the PE-labeled CD45 antibody. The specific CD45-PE staining in this sample is depicted in Figure 3E, where CD45P cells are identified in the top box.The population of CD45+ sputum cells stained with the FITC-labeled isotype control antibody is illustrated in Figure 3F, and the FITC-labeled CD66b antibody is illustrated in Figure 3G. The CD66b+ cells are indicated by the upper box in Figure 3G. To confirm that these cells are granulocytes, CD45+ / CD66b+ cells were sorted using the FACSAria instrument, transferred to a slide by cytocentrifugation, and stained with Wright-Giemsa. As shown in Figure 3H, the cells that were identified with the CD66b+ antibody were indeed granulocytes. The remaining CD45P°s',ivosCD66bne9atlvas cells can include all other types of hematopoietic cells, but are most likely macrophages and monocytes, or lymphocytes, since other hematopoietic cells in sputum are relatively rare (17,27). Specific macrophage markers confirmed that the majority of the cell population in Figure 4A are CD45P°s',ivosCD66bnega,ivos macrophages / monocytes since they express HLA-DR and / or CD11b. Now, referring to Figures 4A to 4G, CD45P°si,ivas sputum cells exposed to either the CD66b probe or the CD206 probe are illustrated. Figures 4A to 4E illustrate a CD66b-negative population that includes a variety of macrophage populations. Figure 4A: CD45P°si,i' / asCD66b-negative sputum cells express HLA-DR and, in some cases, CD11b. Figure 4A illustrates a dot plot showing CD45P°si,ivas / CD66b-nega,ivas sputum cells stained with an isotype control to determine background staining for the anti-HLA antibody. The same isotype control staining is also represented in the histogram of Figure 4B with the light gray curve (I). The dark gray curve in Figure 4B represents the HLA-DR staining of the same cells (C). The rightward shift of the dark gray curve in nzAnnn / Lznz / q / Yi compared to the light gray curve indicates that the cells stain positive for HLA-DR.The isotype control for determining background staining for the anti-CD11b antibody is shown in Figure 4C. The CD45-positive / CD66-negative cell population was divided into small (S) and large (L) cells so that CD11b staining could be better visualized in the fluorescence histograms of Figure 4D and Figure 4E, respectively. The isotype control (I) is represented by the light gray curves in the “S” and “L” histograms, while the anti-CD11b antibody staining (C) is represented by the dark gray curve in the “S” and “L” histograms. Only the small cells stain positive for CD11b. Figures 4F to 4G illustrate an isotype control (dotted diagram on the left) and CD206 staining (dotted diagram on the right) of CD45-positive sputum cells. Figures 4A to 4B illustrate CD45positive CD66bne9a sputum cells that include a variety of macrophage populations.Figure 4A: CD45P°sitivasCD66bne9a,ivas sputum cells express the HLA-DR epitope and in some cases CD11 b. The CD11 b marker is found on myeloid cells. In another modality, the combination of the CD3 / CD19 markers with the CD66b marker allows the identification of possible lymphocyte contamination in the macrophage / monocyte population (the CD66bne9a,ivas / CD3ne9a,ivas / C^ cell subclass in these samples, which happens to host a discernible lymphocyte population (28 to 30). Framing the CD3P°si,ivas / CD19Pos','vas / CD66bP°sitivas cell population from the CD45positive cell population analyzed by the TCPP signal is yet another method to improve the signal with respect to the TCPP marker. Referring to Figure 5, the presence of a CD206-positive cell population is illustrated, coinciding with the presence of numerous macrophages in a sputum smear. Fifteen sputum samples were independently analyzed for the presence of macrophages using Wright-Giemsa and CD206 staining on a flow cytometer. It should be noted that the Wright-Giemsa staining of the sputum smear can be substituted with PAP staining. The number of macrophages counted per slide (solid dots with an x) and the percentage of CD45-positive, CD206-positive cells (solid dots) are plotted for each of the fifteen samples analyzed. The dashed black lines are added to indicate that the data represent the same sample.The absence of macrophages on the slides is represented by white dots, and an inconclusive CD206 profile is represented by a white dot with an x. As shown in Figure 5, when an abundance of macrophages is identified in a sputum smear, a distinct population of CD45+ / CD206+ cells is also observed by flow cytometry. When there are no macrophages on the slide, or when there are very few, the CD45+ / CD206+ profile is unreliable. The presence of a well-defined population of CD45+ / CD206+ cells in the sputum (regardless of size) coincides with a large number of macrophages observed on the slide (>13), indicating a high-quality sputum sample (i.e., from deep within the lung).If a population of CD45+ / CD206+ cells is not present (samples 2, 10, and 11) or is difficult to identify (samples 3 and 4), the sputum smear shows 0 to a few macrophages (<13), indicating that this sputum sample is of lower quality. Fifteen sputum samples were independently analyzed for the presence of macrophages using Wright-Giemsa-stained sputum smears and CD206 staining on a flow cytometer. The number of macrophages counted per slide (solid dots with an X) and the percentage (%) of CD45+ / CD206+ cells (solid dots) are plotted for each of the fifteen analyzed samples. Black dashed lines are added to indicate that the data represent the same sample. The absence of macrophages on the slides is represented by white dots, and an inconclusive CD206 profile is represented by a white dot with an X. Identification of cancer cells in sputum using the CyPath® assay Another component of flow cytometry sputum analysis for early cancer detection is the CyPath® titration of cancer cells. The present inventors analyzed sputum samples obtained from high-risk patients (presumably without lung cancer) to which approximately 3% HCC15 cancer cells were added. For this experiment, outlined in Figure 6, the HCC15 lung cancer cells were labeled with CellMask™ Green so that all cancer cells could be identified in the mixture by this green color. The sputum cells were stained with an anti-CD45-PE antibody so that hematopoietic and non-hematopoietic cells, including the CD45negatiV3S HCC15 cells (data not shown), could be distinguished. After cell fixation, the cell mixture was labeled with TCPP, and the cells were analyzed by flow cytometry. Referring to Figure 6, the experimental plan for sputum analysis with added lung cancer cells is illustrated. HCC15 cancer cells were labeled with CellMask™ Green (step 1), while, in a separate tube, dissociated sputum cells were stained with an PE-labeled anti-CD45 antibody (step 2). After washing away excess CellMask™ Green and anti-CD45 antibody from the respective tubes, the two cell suspensions were blended (step 3). The blended cell suspension was then fixed and incubated with CyPath® solution, which contains TCPP as a fluorescent ingredient (step 4). Figure 6 illustrates a flowchart of the sputum sample preparation for analysis. HCC15 cancer cells were labeled with CellMask™ Green (step 1), while in a different tube, dissociated sputum cells were stained with an anti-CD45 antibody labeled with PE (step 2).After washing off the excess CellMask™ Green and anti-CD45 antibody from the respective tubes, the two cell suspensions were mixed (step 3). The mixed cell suspension was then fixed and incubated with CyPath® Assay solution, which contains TCPP as a fluorescent ingredient (step 4). Now, referring to Figures 7A to 7C, dot plots of sputum cells treated with the CD45-PE marker, CellMask™ Green, and TCPP are illustrated, where lung cancer cells (HCC15) were added to the sample. Figure 7A is a representative dot plot of CD45 expression in sputum cells to which approximately 4% HCC15 lung cancer cells were added. The HCC15 (CD45negative3S) cells were pre-labeled with the fluorescent green dye CellMask™ Green (see Figure 6). The upper frame indicating the CD45-negative cells is based on the appropriate isotype control (see Figure 7D). The lower frame indicates the non-hematopoietic, CD45negative cells. Figure 7B illustrates a dot plot analysis of CD45p0Si,iV3S cells for staining with TCPP (Y-axis) and CellMask™ Green (X-axis).A clearly identifiable population of CD45-positive cells, most likely macrophages, stained positive for TCPP and are in the upper left box. nzAnnn / Lznz / q / Yi Figure 7C illustrates a dot plot analysis of CD45-negative cells for TCPP staining (Y-axis) and CellMask™ Green (X-axis). The CellMask™ Green-positive cells are the HCC15 cells added to the sputum sample, all of which stain positive for TCPP (upper right quadrant). The CellMask™ Green-negative cells are the sputum cells, showing a background staining of 1.2% (lower left quadrant). After applying the three light scattering frames shown in Figures 7A to 7C to the mixture of sputum cells and HCC15 cells, the cells were analyzed for CD45 expression (Figure 7A). TCPP uptake was then determined in both the CD45P°si,iva cell population (population outlined in the upper box) and the CD45ne9a,iva cell population (population outlined in the lower box). Only a small population of CD45P°si,ivas cells showed TCPP uptake (Figure 7B).In contrast, the CD45ne9a cells show a very discrete population of TCPPPitive cells, which also stain positive for CellMask™ Green (Figure 7C, upper right quadrant). Since the only cells treated with CellMask™ Green are HCC15 lung cancer cells, the TCPPPitive CellMask™ GreenP cells are the added HCC15 lung cancer cells. There were no CellMask™ GreenP cells that did not stain with TCPP (Figure 7C, lower right quadrant), indicating that CyPath® stained all the cancer cells added to the sputum sample. Five sputum samples were analyzed in a small pilot experiment: 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 described in Figures 7A–7F, meaning that each sample was treated with CellMask™ Green-labeled HCC15 cells and analyzed as described in Figures 7A–7F. The rationale for adding HCC15 cells to the samples is that these cells serve as a positive control for CyPath® staining. Although there was only one sample C among the five analyzed, the data suggest that a sputum sample from a lung cancer patient differs from one obtained from a patient without the disease: sputum sample C contained more CD45-negative cells and fewer CD45-positive cells than samples harvested from individuals without cancer (Figure 8A).More importantly, sample C showed the highest number of positive TCPPP cells among the negative CD45 cell (epithelial) population. The TCPP screening in the positive CD45 population uniquely distinguishes sample C from the other non-cancerous samples (Figure 8B). Now, referring to Figures 8A and 8B, a preliminary comparative analysis of sputum samples obtained from healthy volunteers and high-risk patients with and without cancer is illustrated. Five samples from different donors were analyzed, similar to the experiment detailed in Figure 6 and Figures 7A through 7F. The white dots represent a sample from a healthy volunteer (H), the black dot samples represent a sample from a high-risk patient without cancer (HR), and the dot with an x ​​represents a sample from a patient with confirmed lung cancer (C). Figure 8A illustrates the total number of CD45-negative (left) and CD45-positive (right) cells in each sample analyzed. Figure 8B illustrates the proportion of positive-tissue proton pump inhibitors (PTPPIs) to CD45-negative (left) and CD45-positive (right) cells in each sample analyzed. Now, with reference to Figures 9A to 9F, a strategy for analyzing sputum cells to detect the presence of positive CD45+ cells is illustrated according to an embodiment of the present invention. Figure 9A illustrates a dot plot of a mixture of sputum cells with HCC15 cells mixed in and treated with an anti-CD45-PE antibody. The upper frame includes the positive CD45+ cells and is based on the appropriate isotype control (not shown). The lower frame indicates the non-hematopoietic, CD45+ cells. Figure 9B represents cells treated with positive CD45+ cells and a cocktail of FITC-labeled probes. The FITC-labeled probes include antibodies directed against CD66b (granulocytes), CD3, and CD19 (lymphocytes).Figure 9B has four quadrants: Cells above the horizontal line are cells that stained positive for TCPP, while cells to the right of the vertical line are cells that stained positive for FITC. Circles are drawn to indicate the different cell populations present in this sample. Figure 9C represents the analysis of the same cells as Figure 9B, represented on a dot plot showing FITC intensity (Y-axis) vs. FSC-A (X-axis; representing cell size). Cell populations are identified between Figure 9B and Figure 9C. Cells in the lower right quadrant show a profile consistent with granulocytes, while cells in the upper right quadrant of Figure 9B show a profile consistent with alveolar macrophages. Figure 9D illustrates the staining with TCPP (Y-axis) vs.FITC fluorescence intensity (X-axis) of CD45-negative sputum cells, including the HCC15 cells added to the sample. Since the CD45-negative sputum cell fraction includes the HCC15 cells, the present inventors expect to find a large population of TCPP-positive cells in this panel. There are two TCPP-positive populations in this sample, as indicated by the circle in the upper left quadrant and the circle in the center and upper right quadrant. Figure 9E illustrates the CD45-negative cell profile as in Figure 9D, but from a control sample that does not include HCC15 cells added to the sample. The cell population in the upper left quadrant of Figure 9D is absent from the dot plot profile of Figure 9E in the upper left quadrant (empty circle). The cells absent from this empty circle are the HCC15 cells.Figure 9F represents the same cell population as Figure 9D, with the dot plot showing CD45-PE intensity (Y-axis) vs. FSC-A (X-axis). The cell population in the upper left and the cell populations in the upper right and center of Figure 9D and Figure 9E are defined in Figure 9F. Figures 9A to 9F suggest that TCPP staining in CD45P°si,ivas cells is related to the alveolar macrophage population. The CD45P°si,i' / as (hematopoietic) cell compartment (Figure 9A) was subdivided into three cell subpopulations based on fluorescence intensity in the FITC and TCPP channels (Figure 9B). When reframed in the CD66b / CD3 / CD19 vs. FSC profile, the population indicated by the lower right circled cell population in Figure 9B that did not stain with TCPP appears to be relatively small cells that stained positive with the CD66b / CD3 / CD19 cocktail (Figure 9C); these cells are likely granulocytes. The other FITC-positive population in Figure 9B (the circled cell population in the upper right that stained positive for TCPP) turned out to be relatively large cells.Their green fluorescence is most likely due to autofluorescence and not to CD66 / CD3 / CD19 staining, as shown earlier with the isotype control profile in Figure 3F. The large size and high autofluorescence suggest that the cell population in the upper right are probably alveolar macrophages (35, 36). The cell population in the lower left of Figure 9B consists of relatively small cells, and since this subpopulation is also CD66 / CD3 / CD19negative, it is probably a cell population of a different subclass of macrophages or monocytes. The CD45negative cells were analyzed similarly (Figures 9C to 9E). Here, HCC15 cells added to the sample were compared with an aliquot that did not include the added HCC15 cells, but was otherwise treated similarly (compare Figures 9C and 9D).The cell population that is absent in the sample without the added HCC15 lung cancer cells is circled. The cells that stained positive for TCPP are medium-sized cells that do not express CD45 and are absent in Figure 9E, as represented by the empty circle in the upper left. The absence of a cell population in the upper left circle for a sample that does not contain HCC15 confirms TCPP staining of the HCC15 cell population (Figure 9E). The other TCPP-positive cell population among the CD45-negative sputum cells (circled in the center / upper right) includes cells similar in size to the HCC15 cells (Figure 9F). These cells are also CD45-negative but can be distinguished from the HCC15 cells by the low levels of autofluorescence in the FITC channel (Figure 9D and Figure 9E). Now, referring to Figures 10A to 10B, quality control (QC) beads are used to establish the bead size exclusion (BSE) frame in the dot plot of Figure 10B. The sputum sample in Figure 10B is framed to remove from analysis those cells that are to the left of the frame placed around the beads approximately 5 µm in size and to the right of the frame placed around the beads 30 µm in size. The sputum samples, controls, isotype controls, and beads are prepared as described below in the Experimental Protocol. Now, referring to Figures 11A through 11F, treated and untreated sputum samples are analyzed by flow cytometry, and the resulting dot plots are illustrated. First, the untreated sputum cells are framed by size using a BSE frame to select cells larger than approximately 5 µm and smaller than approximately 30 µm for further analysis. Figure 11A illustrates a dot plot of sputum cells that fall within this size range. The size frame is referred to as the BSE frame. The BSE frame excludes debris and erythrocytes but not squamous epithelial cells (SECs). Since SECs are dead, they will be removed from the sputum sample analysis using the viability dye FVS510. Figures 11B to 11C illustrate dot plots of sputum cells that were not treated (Figure 11B) and treated (Figure 11C) with BV510 Fluorescence vs. Frontal Scattering.Sputum cells that do not take up the dye are live cells (LCs) and are located below the line in Figure 11C. The live cell frame is referred to as the LC frame. The dye will stain dead cells; live cells are those that do not stain with FVS510. Although this example uses the dye FVS520, other dyes will also work to distinguish the LC population. The threshold above which cells are considered FVS510-positive (and therefore dead) is based on the unstained control (Figure 11B). The majority of cells (95% or more) in the unstained control would be in the LC frame, and less than 5% of the cells (“background staining”) would be outside the LC frame. When this LC frame is then applied to sputum samples that were stained with FVS510, live cells are cells inside the LC frame and dead cells are outside the frame. Figure 11D is a dot plot of an unstained sputum sample used to distinguish single cells from doublets. Cell doublets are considered an event by the flow cytometer, and this event can contain TCPP levels representative of two or more cells. Therefore, doublets can create events with artificially high TCPP content, incorrectly suggesting cancer cells or cancer-associated cells, since TCPP is used as a cancer cell marker. To eliminate doublets, a frame is drawn to identify a single cell (SC) population. A sputum cell profile is created on a FSC-A vs. FSCH dot plot from the acquisition, and BSE / LC frames are applied for SC population analysis.Two diagonal straight lines are drawn along the main axes of the population: one along the top (labeled “upper diagonal” in Figure 11D) and one along the bottom (“lower diagonal”). The lower diagonal runs roughly parallel to the upper diagonal and best starts from the “notch” in the population where cells appear to extend away from the main population to the right (not shown). Cells extending outward (i.e., cells or spots that do not follow the diagonal population) are doublets and must be excluded from the analysis. The SC frame will include only the cells that make up the diagonally oriented population. The SC cells are illustrated in Figure 11D within the diagonal frame.The SC frame is created by connecting two diagonals: one running along the top of the main cell population (indicated by the "upper diagonal") and another following the main cell population at the bottom ("lower diagonal"). To position the lower diagonal, a "notch" must be drawn on the dot plot to indicate the start of the cells that do not follow the main diagonally oriented cell population. Below and to the right of the lower diagonal (the light gray area) are cell doublets that will be excluded from the SC frame. The lower diagonal must cross the notch while following the main diagonal population above and below. Figures 11E to 11F illustrate dot plots of sputum cells treated with either a PE control or a CD45 probe conjugated to a PE fluorophore. Figure 11E is the isotype control. Figure 11F identifies cells as CD45positive (blood cells) or CD45negative (non-blood cells) and is referred to as the CD45 frame. A first sputum sample from the subject was treated with a fluorophore-conjugated CD45 probe and a cocktail of CD66b, CD3, fluorophore-conjugated CD19, fluorophore-conjugated CD206, and TCPP (tube #6). Figures 12A to 12C illustrate dot plots of sputum cells selected by applying the BSE, LC, SC, and CD45 frames to select CD45p+ sputum cells treated with CD66b / CD3 / CD19-FITC-Alexa488 and CD206-PE-CF594 markers. Only cells meeting the criteria of the applied frames were further analyzed. Cell populations were identified based on fluorescence intensity along the CD206 antibody (X-axis) and CD66b / CD3 / CD19 (Y-axis). In each sample, 5 to 6 nzRnnn / Lznz / q / Yi populations were identified. The relative size of each population differs from sample to sample. Figure 12A shows profile 1, where population 1 predominates.Figure 12B shows profile 2, where population 2 predominates. Figure 12C shows profile 3, where CD206+ cells predominate, that is, populations 3 through 6. The predominant populations in each profile type are indicated by a highlighted box in black. Three different signatures for CD45+ sputum cells are represented. Cell populations 5 through 6 are established in light of a isotype control and a control sputum sample, as further identified in the following figures. The presence of macrophages indicates that the sample is from deep within the lung. Table 3 identifies the cell types present in each population. nzAnnn / Lznz / q / Yi Table 3 Population Fluorescence Cell Type FITC / Alexa488 PE-CF594 (Texas Red) 1 Negative Negative Monocytes, macrophages, and other blood cells 2 Positive Negative Granulocytes, lymphocytes 3 Positive Positive Probably macrophages 4 Negative Positive Macrophages 5 Positive High Macrophages 6 High High Macrophages Without reference to Figures 13A to 13B, a dot plot of the isotype control for FITC / ALEXA-488 and sputum cells treated with CD66b / CD3 / CD19 probes conjugated to FITC / ALEXA-488 is illustrated. Figure 13A illustrates a dot plot of CD45-positive cells stained with the FITC / ALEXA-488 isotype control and is presented as FSC on the X-axis versus FITC / ALEXA-488 on the Y-axis. Figure 13B illustrates a dot plot (similar to Figure 11A) of CD45-positive cells stained with a cocktail of antibodies directed against CD66b / CD3 / CD19 (FITC / ALEXA-488) and CD206 (PE-CF594). The horizontal frame of FITC / Alexa488 is established based on the cells above the background stain. The negative frame in the isotype control is set to include approximately 95% of the isotype control cells, while the positive frame is set to include approximately 5% or less of the background.The upper value of the FITC / Alexa488ne9ative frame in CD45-cells of most samples is on average 450, ranging from 100 to 1000. Referring now to Figures 14A to 14B, an isotype control dot plot for PE-CF594 and PE-CF594-labeled sputum cells is illustrated. Figure 14A illustrates a dot plot of CD45-positive cells stained with the isotype controls, presented as FSCs on the X-axis versus PE-CF594 on the Y-axis. Figure 14B is a dot plot (similar to Figure 14A) of CD45-positive cells stained with a PE-conjugated probe / antibody directed against the CD206 cell marker. Figure 14B identifies the frame above which the CD206-positive cell population lies. The upper frame value for PE-CF594-negative CD45 cells in most samples averages 250, ranging from 90 to 500. Now, referring to Figures 14A to 14B, a dot plot is illustrated that establishes the double-negative frame or population 1. Figure 15A is a dot plot that presents CD45-positive sputum cells stained with the isotype control for the FITC / Alexa488 and PE-CF594 (Texas Red) channels, presented as FITC / Alexa488 on the Y-axis vs. PE-CF594 (Texas Red) on the X-axis. Figure 15B is the same dot plot illustrated in Figure 15A and illustrated as a pseudocolor plot of the isotype control, which has been framed by means of the BSE, LC, and CD45-positive cell frames. The horizontal dotted line represents the positive / negative FITC / Alexa488 limit determined in Figures 13A to 13B, while the vertical dotted line is derived from the positive / negative PE-CF594 limit determined in Figures 14A to 14B.The frame for population 1, determined in Figures 15A to 15B, is transferred to the full dot plot and pseudocolor plot of CD45P°positive sputum cells stained with antibodies directed against CD66b / CD3 / CD19 (FITC / Alexa488 - Y-axis) and CD206 (PE-CF594 - X-axis) as illustrated in Figures 16A and 16B, respectively. The upper value of the FITC / Alexa488-negative frame for CD45P°positive cells in most samples averages 600, ranging from 200 to 1050. The upper value of the PE-CF594-negative frame for CD45positive cells in most samples averages 500, ranging from 200 to 750. Now, referring to Figures 16A to 16B, dot plots of a sputum sample as in Figures 15A to 15B, where CD45P+ cells are stained with a cocktail of FITC / Alexa488-conjugated CD66b / CD3 / CD19 antibodies and PE-CF594-conjugated CD206, and analyzed to determine the presence of different cell populations. The cell populations identified as 1 to 5 remain after the application of the BSE, LC, SC, and CD45P+ frames. The same population 1 (box) and boundaries (dotted lines) from Figure 16A are as drawn in Figures 15A to 15B and apply to the profiles shown in Figures 16A to 16B. Figure 16B illustrates the frames for populations 2 through 6 that are established. Populations 3, 5, and 8 are FITC autofluorescent and must be above the horizontal dashed line shown in Figure 16A. Population 4, which is not FITC autofluorescent, must be below the dashed line shown in Figure 16A. Since population 2 is characterized as CD206-negative cells (like population 1) but CD66b / CD3 / CD19-positive, the frame for population 2 is drawn above population 1 and to the right of the PE-CF594 boundary, which is the vertical dashed line in Figure 16A. The box above population 1 formed by the solid line and the dotted line is illustrated in Figure 16B as population 2. Population 5 is identifiable as a completely isolated population to the right of the profile that is both PE-CF594p°sí,ívo and FITCposi,ivo (Figure 16B, frame of population 5).Sometimes population 5 is FITC / Alexa455positive intermediate and in those cases the frame for isolating population 5 crosses the horizontal dotted red line (see figure 17A). Now, referring to Figures 17A to 17C, pseudocolor dot plots of sputum samples that are CD45-positive and treated with probes for CD66b / CD3 / CD19-FITC / Alexa488 from two samples (Figures 17A to 17B are from the same sample but with different frames). All plots show CD45-positive sputum cells that have been framed using the BSE, LC, and SC frames. The dashed horizontal and vertical lines were established at the isotype controls (not shown). Figures 17A to 17B show, in a plot of frames 4 and 5, when the mean FITC fluorescence intensity of population 5 is intermediate and crosses the cutoff line. Figure 17C illustrates an upper right frame of population 6. Referring to Figure 18, each parentheses () on the X-axis reflects the profiles in Figures 12A through 12C. For profile 1, the mean value for each population (population 1, population 2, population 1+2, population 3+4+5+6) is plotted as a percentage (%) of all CD45P+ cells for high-risk (HR) sputum samples. The mean value for each population within a profile group is connected by a straight line. A signature for profile 1 is created by drawing a line between the mean value for each population identified in Figure 18 for profile 1. A similar signature is generated for profiles 2 and 3 for sputum samples from subjects at high risk of developing lung cancer and from subjects identified with lung cancer. Now, referring to Figures 19A through 19C, a comparison of blood cell signatures from sputum collected from a subject at high risk (HR) for developing lung cancer and a subject identified with the cancer (C) is illustrated. Figure 19A illustrates the signature of profile 1 (signature 1) from Figure 18. Figure 19B illustrates a signature of profile 2 (signature 2). Figure 19C illustrates a signature of profile 3 (signature 3). The percentage (%) of cells from population 6 was determined and identified for each signature for the HR and C sputum samples. Figures 20A to 20D illustrate dot plots of sputum cells treated according to tube #7 with CD45, and a cocktail of panCytokeratin-Alexa488 and EpCAM-PE-CF594data and TCPP. The cells represented in the dot plots are those remaining after applying the BSE, LC, SC, and CD45 frames. The dot plot of cells for profiles 1 to 4 (CD45P-positive) is further analyzed, along with the percentage of all CD45-negative cells in each population represented by each profile 1 to 4, and the relative TCPP fluorescence intensity represented by each population. In each sample, nine populations can be identified, as illustrated in Figure 20A. The same nine populations are identified for each profile 2 through 4. The relative size of each subpopulation differs from sample to sample, and each illustrates a different profile (profiles 1 through 4). Figure 20A shows one type of profile where population 1 predominates and comprises more than 80% of all CD45-negative cells. Figure 20B shows another type of profile where population 1 also predominates, but includes less than 80% of all CD45-negative cells; often, a distinct cell population is present in one of the other frames. Figure 20C shows a type of profile where there is still a large population 1 (although less than 80%) but the second largest population is population 2. Figure 20D shows a profile where population 5 is the most predominant population or the second most predominant population after population 1. For each profile there is a different signature.The population that is most important in determining the type of signature is framed in intense color. Figures 21A to 21B illustrate an isotype control plot for CD45ne9a,l sputum cells treated with FITC / Alexa488 or panCytokeratin / Alexa488. Prior to analysis, frames for BSE, LC, SC, and CD45ne9a,l were applied to the analysis population. Two profiles were generated: one featuring front-scattering-A CD45-negative cells (FSC-A) on the X-axis and FITC / Alexa488 on the Y-axis (Figure 21A), and another featuring CD45ne9a,l cells with FSC-A on the X-axis and panCytokeratin / Alexa488 on the Y-axis (Figure 21B). The negative frame in each profile was set to encompass approximately 95% of the cells in the isotype control. The positive frame in each profile includes the remainder of the space above the negative frame and would encompass 5% or less of the background staining. Figures 22A to 22B illustrate an isotype control plot for PE-CF594 and CD45-negative sputum cells framed using BSE, LC, SC, and EpCAM-PE-CF594 cell frames. Prior to analysis, the BSE, LC, SC, and CD45-negative frames were applied to the analysis population. Two profiles were generated: one featuring CD45-negative cells with frontal scatter-A (FSC-A) on the X-axis and PE-CF594 on the Y-axis (Figure 22A), and another featuring CD45-negative cells with FSCA on the X-axis and EpCAM-PE-CF594 on the Y-axis (Figure 22B). The negative frame in each profile was set to encompass approximately 95% of the cells in the isotype control. The positive frame in each profile includes the remainder of the space above the negative frame and would encompass 5% or less of the background staining. Now, referring to Figures 23A to 23B, a dot plot is illustrated with a double-negative frame or population of ΰ045πθ93,'™ε cells. Figure 23A is a dot plot, and Figure 23B is a pseudocolor plot of the isotype control, where the treated sputum sample is analyzed by flow cytometry, and the events representing the cells are framed by BSE, LC, SC, and CD45-negative cells. The horizontal dashed line in Figure 23A represents the FITC / Alexa488 positive / negative boundary determined in Figures 21A to 21B, while the vertical dashed line is derived from the PE-CF594 positive / negative boundary determined in Figures 22A to 22B.The cut-off lines for population 1, determined in Figures 23A to 23B, are incorporated into the full dot plot, and the pseudocolor plot of CD45ne9a,ivastained cells with antibodies directed against all cytokeratins (Alexa488 - Y-axis) and EpCAM (PE-CF594 - X-axis). Now, referring to Figures 24A to 24B, the frames for populations 2 to 9 of CD45ne9a,ivas sputum cells from tube #7 are illustrated. Figure 24A is a dot plot of sputum cells, and Figure 24B is a pseudocolor plot of the same sputum sample as in Figures 23A to 23B, but this time the cells were stained with an antibody directed against all cytokeratins labeled Alexa488 (Y-axis) and an antibody directed against EpCAM labeled PE-CF594 (X-axis). CD45ne9a,ivas cells that were also selected using the BSE, LC, and SC frames are shown. The same population 1 (cells within the solid box) and boundaries (dotted lines extending from it) drawn in Figures 23A to 23B apply to these profiles. Cytokeratin++ cells indicate cells that stain strongly with the panCytokeratin antibody, while EpCAM++ cells stain strongly with EpCAM antibody.Populations 1, 2, and 3 are EpCAM-negative, so they should be above population 1, to the left of the vertical striped line that exists between populations 1 and 6. The difference between the first three populations is that they express different levels of pancytokeratin. The boundary between populations 2 and 3 is determined by identifying cells that are strongly stained with pancytokeratin-Alexa488. The cutoff for CD45-negative cells strongly stained with Alexa488 ranges in fluorescence intensity from 10,000 to 20,000 (average 14,000), and this cutoff determines the lower line of population 3, as well as populations nzAnnn / Lznz / q / Yi and 9. Figure 24A shows a horizontal striped line that separates population 2 from population 3, and above which cells are considered to be strongly stained with the anti-pancytokeratin antibody in this particular sample.The cutoff was determined on the pseudocolor diagram, where a clear cell population is identifiable above the 10,000 fluorescence intensity mark. Populations 1, 6, and 7 are pancytokeratin-negative, and populations 6 and 7 are to the right of population 1 below the horizontal striped line. The difference between populations 1, 6, and 7 is the level of EpCAM expressed in these cells. Population 7 is identified as a population of cells that express EpCAM abundantly, as are populations 8 and 9. The cutoff for cells that express EpCAM abundantly is, on average, 3,000, ranging from 1,000 to 6,000. The vertical striped line in Figure 16A indicates the cutoff for cells that express EpCAM abundantly, thus identifying the left sides of populations 7, 8, and 9.In certain modalities, cells that express FITC abundantly will use 10,000 as the cutoff value for cells that express PE-CF594 abundantly: they use 10 to 15x the value that identifies the upper value of the PE-CF594-negative frame (or the solid vertical line and stripes). Figure 25 illustrates sputum cell dot plots from tube #7 of high-risk subjects, remaining after applying the frames for BSE, LC, SC, and 0045π®93,™ε. The dot plots illustrate profiles 1 to 4 of subjects at high risk of developing lung cancer as shown in Figures 20A to 20D and further analyzed in Figure 26. Figure 26 illustrates a non-blood signature for profile 1 (non-blood signature 1), where the mean value for each population (population 1, population 2, population 5 and PanCK++ (CD45n®9ativas) of the same profile represented in each panel) is identified, and a signature is generated by drawing a line from the mean value of each population within a profile. A signature is generated for each profile 1 to 4. Figure 27 illustrates non-blood signatures for sputum samples from subjects at high risk (HR) for developing lung cancer without the disease (LDCT not indicative of subsequent C) and subjects with lung cancer (C). In signature 4, it can be seen that for the signature of samples C, the arrow in population 5 indicates a decrease in the average expression of EpCAM cells, while the arrow in the pCK population indicates that the average expression of panCytokeratin increased compared to signature 4 of HR. Figures 28A to 28B illustrate the sensitivity and specificity of the presence of PanCK++ 3+4+9 cell populations as a percentage of all CD45n®9a,ivas cells analyzed in sputum samples from subjects at high risk of developing lung cancer and subjects identified with lung cancer. The application of the PanCK++ biomarker to sputum samples yielded a sensitivity of 80% and a specificity of 85% for identifying cancer cells. Figures 29A to 29C illustrate the analysis of cells in a sputum sample obtained from a subject at high risk of developing cancer and a subject with cancer after analyzing the ratio of CD45negative / CD45positive cells (biomarker 1) in the sputum sample. Figure 29A illustrates the CD45negative / CD45positive cell ratio in a sputum sample from a high-risk individual. Figure 29B illustrates the CD45negative / CD45positive cell ratio in a sputum sample from a subject known to have cancer. Figure 29C is an analysis of the CD45negative / CD45positive cell ratio in the sputum sample of two subjects. Figures 30A to 30B illustrate a specificity of 54% and a sensitivity of 90% when the sputum sample from HR and C samples is analyzed with biomarker 1 (CD45negative / CD45positive cell ratio in the sputum sample). Figures 31A to 31C illustrate dot plots of CD45-negative sputum cells from tube #7. Sputum samples were obtained from a subject at high risk of developing cancer and a subject with cancer, and were analyzed after applying the BSE, LC, SC, and CD45-negative frames. The Y-axis is TCPP fluorescence, and the X-axis is pancytokeratin-Alexa488. The presence of TCPP in pancytokeratin-stained CD45-negative cells is biomarker 2. Figure 31A illustrates a dot plot of TCPP-labeled cells in a sputum sample from a high-risk individual. Figure 31B illustrates a dot plot of TCPP-labeled cells in a sputum sample from a subject known to have cancer. Population B indicates the TCPP cell population. Figure 31C is an analysis of the percentage of CD45negative cells in the sputum sample of each subject who are TCPPpositive in population B. Figures 32A to 32B illustrate a specificity of 63% and a sensitivity of 100% for one modality of the method to distinguish a lung cancer (C) sputum sample from a high-risk (HR) (non-lung cancer) sputum sample with the application of biomarker 2 from Figures 31A to 31C. Figures 33A to 33C illustrate a combination of biomarker 1 and biomarker 2 applied to the sputum sample collected as identified in Figures 31A to 31C and Figures 32A to 32B for analyzing a sputum sample obtained from a subject at high risk of developing lung cancer and a subject identified with lung cancer, according to an embodiment of the present invention. Figure 33C illustrates a sensitivity of 90% and a specificity of 90% for identifying the sample from a subject with cancer or a subject without cancer. Figures 34A to 34C illustrate a cancer risk analysis on a sputum sample labeled with CD66b / CD3 / CD19 and CD206 to determine the number of CD66b / CD3 / CD19++ and CD206++ cells in population 6. The horizontal frame for population 6 is set at a mean fluorescence intensity between 10,000 and 30,000 (e.g., between 10,000 to 15,000, or 15,000 to 20,000, or 20,000 to 25,000, or 25,000 to 30,000). The total number of cells in population 6 compared to all CD45positive cells (biomarker 3) present in a sputum sample obtained from a subject at high risk of developing lung cancer (Figure 34A) and a subject identified with lung cancer (Figure 34B) is shown in Figure 34C. Figures 35A to 35B illustrate a specificity of 88% and a sensitivity of 60% for one modality of the method to distinguish a lung cancer (C) sputum sample from a high-risk (HR) (non-lung cancer) sputum sample with the application of the biomarker in Figures 34A to 34C. nzAnnn / Lznz / q / Yi Figures 36A to 36B illustrate a cancer risk analysis of CD45-negative cells from a sputum sample collected from a subject at high risk of developing lung cancer and two subjects identified with lung cancer. The percentage of CD45-negative cells that are pancytokeratin-positive (or abundantly expressing) in the 3+4+9 population is identified as biomarker 4. Figures 37A to 37B illustrate a specificity of 83% and a sensitivity of 80% in one modality of the method to distinguish a lung cancer (C) sputum sample from a high-risk (HR) (non-lung cancer) sputum sample with the application of the biomarker in Figures 36A to 36B. Figures 38A to 38E illustrate a cancer risk analysis of cells from a sputum sample of subjects with cancer and at high risk, with the application of a combination of biomarkers 1, 2, 3 and 4. A specificity of 98% and a sensitivity of 78% are obtained when the combination of biomarkers 1, 2, 3 and 4 is applied to sputum samples, to identify cancer samples from non-cancerous samples. Figure 39 illustrates a flowchart for lung health screening of subjects, including a system and method for fractionating lung cell populations as described herein. In a proof-of-concept clinical study using this screening method (called the CyPath® assay), the fluorescence intensity parameter of CRF in TCPP-labeled lung sputum, combined with data on the patient's smoking history, was able to classify study participants into cancer versus high-risk cohorts with 81% accuracy (12). Although the sensitivity of CyPath®-enhanced sputum cytology was shown to be higher (77.9%) than conventional sputum cytology, the number of cells counted (-600,000) on the stained slides (12 slides / patient) was a limiting factor in the assay's sensitivity.Using a Poisson distribution of CRFs in cancer samples, it is predicted that simply doubling the number of cells per examination to >1 million can increase CRF detection to 95% (12). Furthermore, the need to include a separate sputum smear step for macrophage quantification to verify sample suitability contributes to an assay design with low potential for automation or scalability. Therefore, high-throughput flow cytometry is an alternative to slide-based tests, supporting the examination of millions of cellular events within a clinically relevant timeframe. Experimental protocol Human sputum samples Volunteers were recruited to provide a three-day sputum sample. Three distinct study cohorts were included: 1) individuals at high risk of developing lung cancer, but presumably cancer-free; 2) high-risk individuals diagnosed with lung cancer; and 3) healthy individuals (22 years and older) without a cancer diagnosis and not at high risk of developing lung cancer. To be eligible for the high-risk cohort, subjects had to be heavy smokers, defined as having >30 pack-years and being between 55 and 75 years of age (13). (Examples of 30 pack-years of smoking include: 1 pack per day for 30 years, 2 packs per day for 15 years, etc.). For the healthy subjects cohort, subjects had to have smoked < 5 packs per year and / or abstained > 15 years prior and be 22 years of age or older.Other exclusion criteria (applicable to all cohorts) were the presence of severe obstructive pulmonary disease, uncontrolled asthma, angina with minimal exercise, pregnancy, or working in the mining industry. Sputum collection All study participants were trained in the use of the Acapella® assist device (Smiths Medical, St. Paul, MN) according to the manufacturer's instructions. The Acapella® device is an FDA-approved handheld device that helps thin and mobilize mucus secretions from deep within the lungs. Subjects were instructed to use the device and expel the sputum sample into a sterile collection container. Subjects repeated this procedure at home to collect sputum samples on days two and three. Subjects were instructed to store their specimen container in a cool, dark place or refrigerator and return it to the initial collection site one day after completing the collection. The completed specimen containers were packed with ice packs for frozen transport and shipped overnight for analysis.The cell viability of the samples received from the 3-day collection (n = 38) was on average 64.3% (SD: 25.6%; range: 23.6 to 100%), not including buccal epithelial cells (BEC or cheek cells), which are all dead (14). Sputum dissociation Sputum clots were separated from contaminating saliva using a cotton swab (15, 16). When clot selection was not possible, the entire sample was processed. The sputum was mixed with pre-warmed 0.1% dithiothreitol (DTT) at a ratio of 1:4 to the weight of the sputum clot (w / w), and 0.5% N-acetyl-L-cysteine ​​(NAC) at a ratio of 1:1. The mixture was then shaken for 15 minutes at room temperature. Hank GIBCO® balanced saline solution (HBSS; ThermoFisher Scientific, Waltham, MA) (4 times the volume of the sputum / DTT / NAC mixture) was added, and the resulting cell suspension was shaken for another 5 minutes at room temperature, filtered through a 40–110 μm nylon cell strainer (Falcon, 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 hemocytometer with the trypan blue exclusion method to determine cell viability. Sputum smear The same cotton swabs used to transfer the sputum clots for processing were used to transfer the sputum cells to a slide. Using an additional slide, the sputum sample was rubbed between the two slides to cover a large portion of both (16). The slides were air-dried and stained with Wright-Giemsa. One or both slides were read, and the pathologist determined the number of macrophages counted. Other human samples Blood Two 7 mL vials of peripheral blood were obtained from healthy volunteers. Most of the blood was used to obtain white blood cells (WBCs) by lysing red blood cells (RBCs) with BD Pharm Lyse™ (BD nzRnnn / Lznz / q / Yi Biosciences, San Jose, CA). The remainder was used as a source of RBCs. Saliva BECs were collected from the oral mucosa of healthy volunteers by scraping the inside of their cheek with a cell spatula. The saliva containing the BECs was processed using the same protocol as for sputum cell dissociation. Lung cancer cells HCC15 lung cancer cells (ATCC, Manassas, VA) were grown in RPMI 1640, supplemented with 10% fetal bovine serum and 1% penicillin / streptomycin in an incubator, at 5% CO2, set at 37°C. Antibody and reagents for flow cytometry Examples of antibodies that can be used to stain sputum cells were the PE-labeled antibody directed against the pan-leukocyte cell surface marker CD45 (anti-CD45-PE), anti-CD66b-FITC to identify granulocytes, anti-CD206-FITC, anti-HLA-DR-BV421, anti-CD11 b-BV650, anti-CD11b-APC and anti-CD11c-BV650 to label macrophages, 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, as well as their respective isotype controls, were acquired from BioLegend (San Diego, CA), while anti-CD11c, anti-CD66b, anti-CD206, anti-HLA-DR, and their respective isotype controls were acquired from BD Biosciences. Additional antibodies are listed in Table 2. Tetra(4-carboxyphenyl)porphyrin (TCPP) was purchased from Frontier Scientific (Logan, UT) and CellMask™ Plasma Membrane Stains from ThermoFisher Scientific. Megabead NIST Traceable Particle Size Standards (5, 10, 20, 30, 40, and 50 µm) were purchased from Polysciences, Inc. (Warrington, PA). All antibodies were titrated in sputum cells and, in some cases, in blood cells (CD3 and CD19) to determine the optimal staining concentration to reflect the greatest difference in fluorescence intensity compared to their isotype controls. The optimal concentration of TCPP and EpCAM was titrated in sputum cells and HCC15 cells. The other staining reagents and beads were used according to the manufacturer's recommendations. Flow cytometry analysis and cell sorting Characterization of cell populations Referring to Figure 1C, the cells were analyzed by flow cytometry, where each cell passes through a laser beam and the laser light scattering is detected by forward scatter detectors (FSCs) and side scatter detectors (SSCs). The size and granularity of the cells can be characterized as illustrated in Figure 1E. Sputum sample cells can be fractionated based on the presence of live cells (LC) and dead cells (DC), and whether they are single cells (SC) or double cells, captured as an event as described herein. Single cell suspension samples from dissociated sputum samples from figures nzAnnn / Lznz / q / Yi 2A to 21 and Figures 9A to 9F were incubated with one or more of the following probes: anti-CD45-PE at approximately 1 pg / mL, anti-CD66b-FITC at approximately 3 pg / mL, and anti-HLA-DR-BV421 (5 pg / mL), anti-CD11 b-APC (4 pg / mL), anti-CD11C-BV650 (5 pg / mL), or a mixture of anti-CD3-Alexa Fluor 488 (2 pg / mL) and anti-CD19-Alexa Fluor 488 (2 pg / mL). In a separate tube, single-cell suspensions from dissociated sputum samples were incubated with anti-CD45-PE at approximately 1 pg / mL and anti-CD206-FITC at 4 pg / mL for sputum quality determination. All incubations were performed on ice for 35 minutes, protected from light. After washing the cells with HBSS, they were fixed for 30 minutes with 1% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA) at 4 °C. The cell suspensions were then washed in cold HBSS and kept on ice until analysis. Dizziness with TCPP / CyPath of sputum samples with added HCC15 Referring to Figures 1A to 1E and Figures 9A to 9F, dissociated sputum cells were labeled with anti-CD45 antibody and fixed as described above. HCC15 cells were harvested with trypsin, washed with DPBS (ThermoFisher Scientific), and labeled with CellMask™ Green plasma membrane stain. CellMask™ Green-labeled HCC15 cells (cmgHCC15) were fixed with 1% paraformaldehyde for 30 minutes at 4°C and washed with HBSS. Some of the sputum cell suspensions were supplemented with 3% cmgHCC15 cells. The fixed cell pool was then incubated with cold TCPP (4 pg / mL) for 1 hour at 4°C. After titration, the cells were washed and placed on ice until further analysis. In one modality, samples were analyzed using a BD LSR-II flow cytometer (BD Biosciences) equipped with four lasers (404 nm, 488 nm, 561 nm, and 633 nm). Cell classification of the whole sputum, specifically the CD45+CD206+, CD45+CD66b+, or CD45+CD66b+ subpopulations, was performed on a BD FACSAria cell classifier (BD Biosciences). Post-collection data analysis was performed using FlowJo software (Tree Star, Inc., Ashland, OR). Cytology Whole sputum samples were prepared using the sputum dissociation method described above. Cytocentrifuge slides with 1 and 2.5 x 10⁵ cells per slide were prepared using a Cytopro 7620 (Wescor, Logan, UT) or a Hettich 32A (Rotofix, Beverly, MA) cytocentrifuge. The slides were stained with Wright or Wright-Giemsa stain, following the manufacturer's protocols. Images were produced at room temperature using a Nikon Eclipse Ti or an Olympus BX40 microscope. The Nikon microscope was equipped with a UPIanApo20X / 0.7 objective and a DS-RI2 camera, and the Olympus microscope with a PLAPO60X / 1.4 objective and an SD100 camera. NIS-Elements Advanced Research (Nikon) and CellSens Standard (Olympus) were used to ensure image quality. Macrophages have traditionally been used to verify the suitability of a sputum sample. The Papanicolaou Society of Cytopathology guideline for evaluating sputum samples by cytology states that: “No numerical cutoff point for the number of macrophages is consistently reported in the literature, but an adequate specimen should have many readily identifiable cells of this type” (31). HLA-DR and CD11 b (or CD11 c), along with CD14 and CD206, nzAnnn / Lznz / q / Yi, have been shown to be useful markers for the flow cytometry identification of different macrophage and monocyte subclasses within the lung (32, 33). CD206 is a specific marker of alveolar macrophages, which are long-lived cells that have populated the lung since embryonic development (34). CD206P°positive macrophages, although of hematopoietic origin, cannot be found in circulating blood.This macrophage population is specific to lung tissue (34) and is therefore a good candidate to serve as a measurement to verify the suitability of the sample. Sputum sample preparation The samples are prepared for analysis as described in Figures 10A and 10B through Figure 39. In summary, sputum cells are received, processed, and labeled with antibody, and dye staining is performed on day 1. The samples are treated with TCPP and analyzed by flow cytometry on day 2. The sputum samples analyzed in Figures 10A and 10B through Figure 39 are processed as described below. The samples are analyzed on a flow cytometer that has at least one laser, or at least two lasers, or at least three lasers, and a plurality of channels, for example, 5 channels, or at least 5 channels, but is not limited to these. Sputum dissociation Sputum samples are weighed, and based on the weight, the dissociation reagents are added as follows: 1 volume of 0.5% NAC solution and 4 volumes of 0.10% DTT solution. The sample is vortexed and shaken at room temperature. Subsequently, 4 volumes of 1X Hank's Balanced Saline Solution (HBSS) are added, based on the total volume (sputum solution + NAC + DTT). The sample is filtered and then centrifuged at 800x g for 10 minutes. The supernatant is aspirated, and the pellet is resuspended with HBSS according to the sample size (e.g., small sample (< 3 g): add 250 pL of HBSS; medium sample (> 3 - < 8 g): add 760 pL of HBSS; large sample (> 8 g): add 1460 pL of HBSS). A 1:10 dilution is used to determine cell yield. 0.5% N-acetyl-L-cysteine ​​(NAC) solution: Add 0.85 g of sodium citrate dihydrate to 45 mL of dry water, 500 mL of 3M NaOH, and 0.25 g of NAC, and stir until dissolved. The pH of the solution should be between approximately 7.0 and 8.0, and the volume is adjusted to 50 mL with dry water. Dithiothreitol (DTT) 0.10% solution: Add 0.10 g of DTT to 100 mL of HaOdd and stir until dissolved. Divide the solution into 10 mL aliquots and store frozen at -20 °C until use. Prepare a 1 mg / mL TCPP stock solution for CyPath as follows: Add 25 mL of isopropanol and 0.2 g of sodium bicarbonate to 25 mL of H₂O and stir until dissolved. Adjust the pH of the solution to approximately 9–10 if necessary. Add 0.05 g of TCPP, protect the solution from light, and stir until dissolved. Table 4 indicates the pL of cells to be distributed in aliquots in the tubes for counting and titration with antibody. nzRnnn / Lznz / q / Yi Table 4. Cell volume (pL) to be aliquoted into counting and titration tubes with antibodies Sample Size Function: Small (3g) Medium (>3 - < 8g) Large (> 8g) Count 5 10 10 Tipping: Unstained Control (#4)* 20 50 50 Isotype Control (#5)* 20 50 50 Blood Cell Analysis (#6)* 115 350 725 Epithelial Cell Analysis (#7)* 115 350 725 nzRnnn / Lznz / q / Yi * These numbers indicate the flow cytometer tube number Antibody / Dizziness with FVS Sputum cells are distributed into aliquots according to Table 4 for the reagents indicated in Table 5, which are added to prepare the experimental and control tubes for titration of dissociated sputum cells. Table 5: Tidal reagents Marker Antibody Clone [Stock] (pg / mL) [Final] (pg / mL) Dilution Factor Viability Stain Fixed Viability Stain (FVS510) - 1000X Leukocytes PE anti-human CD45 (IgG1) HI30 10 1 1:10 Granulocytes FITC anti-human CD66b (IgG1) 80H3 100 3 1:33 T Cells Alexa488 anti-human CD3 (IflG1) UCHT1 200 2 1:100 B Cells Alexa488 anti-human CD19 (IgGD) HIB19 200 2 1:100 Macrophages PE-CF594 anti-human CD206 (IgGD 19.2) 200 3 1:66.7 Epithelial Cells PE-CF594 anti-human EpCAM (IgG EBA-1 50 1 1:50 Alexa488 anti-human cytokeratin (panCK) C-11 500 4 1:125 panCK, CD3 and CD19 Isotype Alexa488 IgGlK MOPC21 200 4 1:50 CD66b Isotype FITC IgGlK MOPC21 50 3 1:16.7 CD206 / EpCAM Isotype PE-CF594 IgGlK X-40 200 3 1:66.7 CompBead Plus Compensation Beads - - 1 drop HBSS - - 1 X - Flow-Fix PFA 1% - - 1% 1% Table 6, and Table 7 and Table 9: Samples for bead size, flow cytometer compensation, isotype control, sputum background and treated sputum are prepared as described. Table 6: Tubes for instrument configurations Tube Comp Bead Plus (+) Comp Bead Plus (-) antiCD45 (pL) antiCD66b (pL) antiCD3 (pL) antiCD19 (pL) antiCK (pL) antiCD206 (pL) AntiEpCAM (pL) HBSS (pL) Total Volume (pL) # Name 1 PE beads 1 drop* 1 drop 4 76 200 2 LEAVE BLANK 3 PE-CF594 - beads 1 drop 1 drop 4 4 72 200 * 1 drop = 60 pL nzAnnn / Lznz / q / Yi Table 7: Tubes for sample analysis Tube Sputum Clot Weight (Step 4) Cell Volume (pL) HBSS (pL) FVS510 (pL) Antibodies (pL) # Name antiCD45 Isotype PE-CF594 Alexa488 Isolipe FITCI Isotype 4 Unstained Small 20 80 - - - - - Medium 50 50 Large 50 50 5 Isotype Controls Small 20 59.9 0.6 10 1.5 2 6 Medium 50 29.9 0.6 Large 50 29.5 1 6 CyPath Assay (Blood Cells) antiCD45 antiCD206 antiCD3 antiCD19 AntiCD66 Small 115 92.25 1.5 25 3.75 2.5 2.5 7.5 Medium 350 64.5 3 50 7.5 5 5 15 Large 725 100 10 100 15 10 10 30 7 CyPath Assay (Epithelial Cells) antiCD45 AntiEpCAM AntipanCK Small 115 101.5 1.5 25 5 * Medium 350 83 3 50 10 4 Large 725 137 10 100 20 Tubes #1 to #7 are incubated in the dark for 35 min. After antibody incubation, each tube is filled with cold HBSS, and the supernatant is centrifuged at 800 x g for 10 min at 4 °C. The supernatant is discarded, and the pellet is resuspended as follows: To tubes #1 to #3, add 0.5 mL of cold HBSS to each tube and store on ice at 4 °C until flow cytometry data acquisition. To tubes #4 and #5, add 2 mL of 1% PFA fixative. To tubes #6 and #7, add 10 mL of 1% PFA fixative. Incubate the tubes for 1 hour on ice, covered with a thin film. After fixative incubation, fill each tube with cold HBSS. Centrifuge the cells at 1600 x g for 10 min at 4 °C. Aspirate as much of the supernatant as possible without disturbing the sediment. Resuspend the sediment in the residual fluid. Resuspend tubes #4 and #5 at 0.Add 2 mL of cold HBSS and store with tubes #1 to #3 on ice at 4 °C, until data acquisition by flow cytometry. For tubes #6 and #7, add ice-cooled HBSS according to the following formula: Final volume (mL) of each tube = 0.15* [Total cells / 106] (formula 1) | fe s For the cell count, obtain the cell count from a cell suspension diluted 1:40 with trypan blue. Add 10 pL of the 1:40 dilution to a hemocytometer and count the cells in the four large quadrants. An accurate cell count is 25 to 60 cells per quadrant. Place tubes #6 and #7 on ice overnight at 4°C until they are ready for TCPP tidal testing on day 2. nzRnnn / Lznz / q / Yi Table 9: Dizziness with TCPP / instrument reagents Reagent Company HBSS Gibco Pearls 30 pm NIST Polysciences Pearls 20 pm NIST Polysciences Pearls 5 pm NIST Polysciences Pearls Rainbow Spherotech The CyPath assay TCPP working solution is prepared as a 20 pg / mL TCPP solution (1:50 of the stock solution) using chilled HBSS and protected from light. Obtain one tube of A549 cells for use as an unstained control for tidal dysplasia with FVS and TCPP (tube #8). Obtain one tube of A549 cells for use as an offset tube for tidal dysplasia with FVS (tube #9). Obtain one tube of A549 cells for use as an offset tube for tidal dysplasia with TCPP (tube #10). Obtain one tube of A549 cells for use as an offset tube for tidal dysplasia with PanCK (tube #11). TCPP Tidal Dysplasia Add the volume of TCPP working solution from the CyPath assay according to Table 10. Table 10: Tide volumes with TCPP Tube # Cells TCPP Working Solution Volume Actual Volume Used 6 Sputum Sample Volume as calculated in formula 1 7 Sputum Sample Volume as calculated in formula 1 10 A549 300 pL 300 pL Incubate the samples 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 residual fluid and add 300 µL of cold HBSS to tube #10. If the total cell count is <20 x 10⁶ cells, then add 250 µL of cold HBSS to tubes #6 and #7 to transfer the cells from the 15 mL conical tube to a flow cytometry tube (labeled #6 and #7, respectively). Acquisition of flow cytometry data A flow cytometry data acquisition rate of 10,000 events / second or less is preferred with the following settings: The parameters used in the LSRII include: Threshold, FSC voltage, SSC voltage, BV510 voltage (which must be verified in ALL cells, including BECs), PE voltage, FITC voltage, PE-TxRed voltage, and APC voltage. To optimize the assay using equivalent flow cytometers, the technician will be familiar with the preferred settings to obtain equal or similar results. Summary of fluorescence intensity values ​​that determine population frames: nzAnnn / Lznz / q / Yi BLOOD: 6 marks Fluorophore Average Range To establish population 1 FITC 600 200 to 1050 PE-CF594 500 200 to 750 To establish population 5 FITC (limit with population 6) 3300 1,000 to 6,000 PE-CF594 (left limit) 13,000 8,000 to 20000 EPITHELIAL: 9 frames Fluorophore Average Range To establish population 1 FITC 250 90 to 500 PE-CF594 450 100 to 1000 To establish the high expression limit FITC 14,200 10,000 to 30,000 PE-CF594 3,000 1,000 to 6,000 (= 10 to 20 x PECF594 value that determines population 1) It should be noted that the adjustments referred to are specific to the LSRII instrument and may vary for other flow cytometers, but it will be obvious to a technician in the field how to compensate for the different instruments to produce comparable intervals. Although the preceding examples are exemplary for lung cancer detection, other lung diseases and conditions can be detected or monitored over time using the system and method described herein. For example, when a subject is suspected of developing or being prone to an exacerbation of symptoms associated with lung diseases, such as asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, or graft-versus-host disease, sputum can be analyzed for alterations in the distribution of cell populations, compared to a database of control (non-disease) and disease sample profiles. It should be noted that, in the specification and claims, "around" or "approximately" means within twenty percent (20%) of the stated numerical quantity. All computer software described herein may be incorporated on any computer-readable media (including combinations of media), including, without limitation, CD-ROM, DVD-ROM, hard disks (local or network storage devices), USB flash drives, other removable drives, ROM, and firmware. In at least one embodiment, and as will be readily understood by a person skilled in the art, the apparatus according to the invention shall include a general-purpose or special-purpose computer or a distributed system programmed with computer software that implements the steps described above, the computer software being in any appropriate computer language, including C++, FORTRAN, BASIC, Java, assembly language, microcode, distributed programming languages, etc. The apparatus may also include a plurality of such computers / distributed systems (e.g., connected via the Internet or one or more intranets) in a variety of hardware implementations.For example, data processing can be performed by an appropriately programmed microprocessor, cloud computing, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or similar, along with appropriate memory, network, and bus elements. Multidimensional data recorded from the cells and particles analyzed as they move through the flow cytometer are captured, enabling the analysis and fractionation of cell populations based on multidimensional optical properties. References 1. National Lung Screening Trial Research Team, Church TR, Black WC, Aberle DR, Berg CD, Clingan KL, et al. Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med. May 2013 23;368(21):1980-91. 2. Krantz SB, Meyers BF. Health risks from computedtomographic screening. Thorac Surg Clin. Mayo 2015;25(2):155-60. 3. Sacher AG, Komatsubara KM, Oxnard GR. Application of Plasma Genotyping Technologies in Non-Small Cell Lung Cáncer: A Practical Review. J Thorac Oncol. 1 Sep 2017;12(9):1344-56. 4. L¡ T, Kung H-J, Mack PC, Gandara DR. Genotyping and Genomic Profiling of Non-SmallCell Lung Cáncer: Implications for Current and Future Therapies. J Clin Oncol. 10 Marzo 2013;31 (8): 103949. 5. The Clinical Lung Cáncer Genome Project (CLCGP) y NetWork Genomic Medicine (NGM). A Genomics-Based Ciassification of Human Lung Tumors. Sci Transí Med. 30 Oct 2013;5(209):209ra153. 6. Speicher MR, Pantel K. Tumor signatures in the blood. Nat Biotechnol. Mayo 2014;32(5):441-3. 7. Stahl DL, Richard KM, Papadimos TJ. Complications of bronchoscopy: A concise synopsis. Int J Crit IIIn Inj Sci. 2015;5(3):189-95. nzRnnn / Lznz / q / Yi 8. Thunnissen FBJM. Sputum examination for early detection of lung cáncer. J Clin Pathol. Nov 2003;56(11):805-10. 9. Gao W, Keohavong P. Detection of point mutations of K-ras oncogene and p53 tumorsuppressor gene in sputum samples. Methods Mol Biol Clifton NJ. 2014;1105:325-44. 10. L¡ G, Guillaud M, LeRiche J, McWilliams A, Gazdar A, Lam S, et al. Automated Sputum Cytometry for Detection of Intraepithelial Neoplasias in the Lung. Anal Cell Pathol Amst. 2012;35(3):187201. 11. Osterloh J, Vicente M. Mechanisms of porphyrinoid localization in tumors. J Porphyr Phthalocyaniones. 2002;6:305-24. 12. Patriquin L, Merrick DT, Hill D, Holcomb RG, Lemieux ME, Bennett G, et al. Early Detection of Lung Cáncer with Meso Tetra (4-Carboxyphenyl) Porphyrin-Labeled Sputum. J Thorac Oncol. 1 Sep 2015;10(9):1311-8. 13. Wood DE. National Comprehensive Cáncer NetWork (NCCN) Clinical Practice Guidelines for Lung Cáncer Screening. Thorac Surg Clin. Mayo 2015;25(2):185-97. 14. Paszkiewicz GM, Timm EA, Mahoney MC, Wallace PK, Nasca MAS, Tammela TL, et al. Increased Human Buccal Cell Autofluorescence Is a Candidate Biomarker of Tobacco Smoking. Cáncer Epidemiol Prev Biomark. Enero 2008; 1 ;17(1 ):239-44. 15. Pizzichini E, Pizzichini MM, Efthimiadis A, Hargreave FE, Dolovich J. Measurement of inflammatory índices in induced sputum: effects of selection of sputum to minimize salivary contamination. Eur Respir J. 1996 Jun 1 ;9(6):1174-80. 16. Thomas H. Sputum: preparation and examination of Gram stained smears [Internet]. Disponible en: https: / / www.uvmhealth.org / medcenter / Documents / 8138Sputum_Gram_Stain.ppt 17. Franks TJ, Colby TV, Travis WD, Tuder RM, Reynolds HY, Brody AR, et al. Resident cellular components of the human lung: current knowledge and goals for research on cell phenotyping and function. Proc Am Thorac Soc. 15 Sep 2008;5(7):763-6. 18. Korbelik M, Krosl G, Chaplin DJ. Photofrin Uptake by Murine Macrophages. Cáncer Res. Mayo 1991 1 ;51 (9):2251-5. 19. KorbelikM, Krosl G, Olive PL, Chaplin DJ. Distribution of Photofrin between tumour cells and tumour associated macrophages. Br J Cáncer. Sep 1991 ;64(3):508-12. 20. Figge FHJ, Weiland GS, Manganiello LOJ. Cáncer detection and therapy; affinity of neoplastic, embryonic, and traumatized tissues for porphyrins and metalloporphyrins. Proc Soc Exp Biol Med Soc Exp Biol Med N Y N. 1948 Aug;68(3):640. 21. Rassmussen-Taxdal DS, Ward GE, Figge FHJ. Fluorescence of human lymphatic and cáncer tissues following high doses of intravenous hematoporphyrin. Cáncer. Enero 1955; 1 ;8(1 ):78-81. 22. Altman Kl, Salomón K. Localization of a halogenated porphyrin in mouse tumours. Nature. 24 Sep1960;187:1124. 23. Schupp J, Krebs FK, Zimmer N, Trzeciak E, Schuppan D, Tuettenberg A. Targeting myeloid cells in the tumor sustaining microenvironment. Cell Immunol [Internet]. 1 Nov 2017 [citado el 30 Nov 2017]; nzRnnn / Lznz / q / Yi Disponible en: http: / / www.sciencedirect.com / science / article / pii / S0008874917301909 24. Ward-Hartstonge KA, Kemp RA. Regulatory T-ceíí heterogeneity and the cáncer immune response. Clin Transí Immunol. 15Sep 2017;6(9):e154. 25. Barnes TA, Amir E. HYPE or HOPE: the prognostic valué of infiltrating immune cells in cáncer. Br J Cáncer. 8 Agosto 2017;8;117(4):451-60. 26. Murray PJ. Nonresolving macrophage-mediated inflammation in malignancy. FEBS J. :n / an / a. 27. Brooks CR, van Dalen CJ, Hermans IF, Douwes J. Identifying leukocyte populations in fresh and cryopreservedsputum using flow cytometry. Cytometry B Clin Cytom. 1 Marzo 2013;84B(2):104-13. 28. Vidal S, Bellido-Casado J, Granel C, Crespo A, Plaza V, Juárez C. Flow cytometry analysis of leukocytes in inducedsputum from asthmaticpatients. Immunobiology. 2012 Jul;217(7):692-7. 29. Leckie MJ, Jenkins GR, Khan J, Smith SJ, Walker C, Barnes PJ, et al. Sputum Tlymphocytes in asthma, COPD and healthy subjects have the phenotype of activated intraepithelial T cells (CD69+ CD103+). Thorax. Enero 2003;58(1 ):23-9. 30. Shiota Y, Matsumoto H, Hiyama J, Okamura Μ, Ono T, Mashiba H. Flow cytometric analysis of lymphocytes and lymphocyte subpopulations in induced sputum from patients with asthma. Allergol Int. 2000;49:125-33. 31. Papanicolaou Society of Cytopathology Task Forcé, on Standards of Practice. Guidelines of the Papanicolaou Society of Cytopathology for the examination of cytologic specimens obtained from the respiratory tract. Papanicolaou Society of Cytopathology Task Forcé on Standards of Practice. Diagn Cytopathol. 1999 Jul;21(1 ):61-9. 32. Freeman CM, Crudgington S, Stolberg VR, Brown JP, Sonstein J, Alexis NE, et al. Design of a multi-center immunophenotyping analysis of peripheral blood, sputum and bronchoalveolar lavage fluid in the Subpopulations and Intermedíate Outcome Measures in COPD Study (SPIROMICS). J Transí Med. 27 Ene 2015: 27;13:19. 33. Yu Y-RA, Hotten DF, Malakhau Y, Volker E, Ghio AJ, Noble PW, et al. Flow Cytometric Analysis of Myeloid Cells in Human Blood, Bronchoalveolar Lavage, and Lung Tissues. Am J Respir Cell Mol Biol. Enero 2016;54(1 ):13-24. 34. Desch AN, Gibbings SL, Goyal R, Kolde R, Bednarek J, Bruno T, et al. Flow Cytometric Analysis of Mononuclear Phagocytes in Nondiseased Human Lung and Lung-Draining Lymph Nodes. Am J Respir Crit Care Med. 15 Marzo 2016;193(6):614-26. 35. Patel VI, Booth JL, Duggan ES, Cate S, White VL, Hutchings D, et al. Transcriptional Classification and Functional Characterization of Human Airway Macrophage and Dendritic Cell Subsets. J Immunol. 1 Feb 2017;198(3):1183-201. 36. Baharom F, Rankin G, Blomberg A, Smed-Sórensen A. Human Lung Mononuclear Phagocytes in Health and Disease. Front Immunol [Internet]. 1 Mayo 2017 [citado 13 Dic 2017];8. Disponible en: https: / / www.ncbi.nlm.nih.gov / pmc / articles / PMC5410584 / 37. Lay JC, Peden DB, Alexis NE. Flow cytometry of sputum: assessing inflammation and nzRnnn / Lznz / q / Yi immune response elements in the bronchial airways. Inhal Toxicol. Jun 2011 ;23(7) :392-406. 38. Erozan YS, Frost JK. Cytopathologic diagnosis of cáncer in pulmonary material: a critical histopathologic correlation. Acta Cytol. Dic 1970;14(9):560-5. 39. Hinson KF, Kuper SW. THE DIAGNOSIS OF LUNG CANCER BY EXAMINATION OF SPUTUM. Thorax. Dic 1963;18:350-3. 40. Johnston WW, Bossen EH. Tenyears of respiratory cytopathology at Duke University Medical Center. I. The cytopathologic diagnosis of lung cáncer during the years 1970to 1974, noting the signif¡canee ofspecimen number and type. Acta Cytol. Abril 1981 ;25(2):103-7. 41. Farber SM. Clinical appraisal of pulmonary cytology. JAMA. 4 Feb 1961 ;175:345-8. 42. Ng AB, Horak GC. Factors significant in the diagnostic accuracy of lung cytology in bronchial washing and sputum samples. II. Sputum samples. Acta Cytol. Agosto 1983;27(4):397-402. 43. Wheater P R, Burkitt HG, Daniels V G. Functional histology. Primera edición Norwich, Inglaterra: Jarrold y Sons Ltd; 1979. 44. Krombach F, Münzing S, Allmeling AM, Gerlach JT, Behr J, Dórger M. Cell size of alveolar macrophages: an interspecies comparison. Environ Health Perspect. Sep 1997;105 Suppl 5:1261-3. 45. Kini SR. Color atlas of pulmonary cytopathology. Springer-Verlag Nueva York, INc.; 2002. 301 p. 46. Fillmore CM, Xu C, Desaí PT, Berry JM, Rowbotham SP, Lin Y-J, et al. EZH2 inhibition sensitizes BRG1 and EGFR mutant lung tumors to Topoll inhibitors. Nature. 9 Abril 2015;520(7546):23942. Although the invention has been described in detail with particular reference to these embodiments, other embodiments may achieve the same results. Variations and modifications of the present invention will be obvious to those skilled in the art, and the appended claims are intended to cover all such modifications and equivalents. Full descriptions of all references, applications, patents, and publications cited above are incorporated herein by reference.

Claims

CLAIMS 1. A method for predicting the probability of lung disease in a subject, said method comprising the steps of: labeling an ex vivo sputum sample with one or more of the following: i) a first labeled probe that binds to a biomarker expressed on a leukocyte population in 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 in sputum cells, a T cell probe that binds to a biomarker expressed on a T cell population in sputum cells, a B cell probe that binds to a biomarker expressed on a B cell population in 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-related cell in a sputum sample;(v) a fifth labeled probe that binds to a biomarker expressed in a population of epithelial cells from the sputum cells; (vi) a sixth labeled probe that binds to a cell surface biomarker expressed in a population of epithelial cells from the sputum cells; analyzing the labeled sputum sample by flow cytometry to obtain data comprising cell-level cytometric data, based on a mean fluorescent signature of any of the labeled probes (i) to (vi); and, from the cell-level data, detecting the probability of lung disease in a subject based on a profile of the presence or absence of the labeled probes in the labeled cell-level data.

2. The method according to claim 1, further comprising determining the proportion of sputum cells in the data collected from the labeled sputum sample that are negative for i), compared to sputum cells that are positive for i), to identify a biomarker 1.

3. The method according to claim 2, wherein a proportion less than 2 indicates that the sputum sample is positive for biomarker 1.

4. The method according to claim 3, wherein the positive biomarker 1 has a sensitivity of at least approximately 80% and a specificity of at least 50%.

5. The method according to claim 1, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are negative for i) and positive for iv) and v), in order to identify a biomarker 2.

6. The method according to claim 5, wherein a percentage of sputum cells nzRnnn / Lznz / q / Yi negative for i) and positive for iv) and v) that is greater than 0.03%, indicates that the sputum sample is positive for biomarker 2.

7. The method according to claim 6, wherein the positive biomarker 2 has a sensitivity of at least 90% and a specificity of at least 50%.

8. The method according to claim 3, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are negative for i) and positive for iv) and v), to identify a biomarker 2.

9. The method according to claim 8, wherein a percentage of sputum cells negative for i) and positive for iv) and v) that is greater than 0.03% indicates that the sputum sample is positive for biomarker 2.

10. The method according to claim 9, wherein a combination of positive biomarker 1 and positive biomarker 2 has a sensitivity of at least 80% and a specificity of at least 80%.

11. The method according to claim 1, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are positive for i), iii) and that exhibit autofluorescence for FITC, to identify a biomarker 3.

12. The method according to claim 11, wherein a percentage of sputum cells positive for i), iii) and exhibiting autofluorescence for FITC, which is greater than 0.03%, indicates that the sputum sample is positive for biomarker 3.

13. The method according to claim 12, wherein the positive biomarker 3 has a sensitivity of at least 60% and a specificity of at least 70%.

14. The method according to claim 9, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are positive for i), iii) and v), to identify a biomarker 3.

15. The method according to claim 14, wherein a percentage of sputum cells positive for i), iii) and exhibiting autofluorescence for FITC, which is greater than 0.03%, indicates that the sputum sample is positive for biomarker 3.

16. The method according to claim 15, wherein the combination of positive biomarkers 1, 2 and 3 has a sensitivity of at least 80% and a specificity of at least 80%.

17. The method according to claim 1, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are negative for i) and positive for v) and vi), to identify a biomarker 4.

18. The method according to claim 17, wherein the percentage of cells negative for i) and positive for v) and vi) that is greater than 2% indicates that the sample is positive for biomarker 4.

19. The method according to claim 18, wherein the positive biomarker 4 has a sensitivity of at least 70% and a specificity of at least 70%. nzRnnn / Lznz / q / Yi 20. The method according to claim 15, further comprising determining, from the data collected from the labeled sputum sample, the sputum cells that are negative for i) and positive for v) and vi), to identify a biomarker 4.

21. The method according to claim 20, wherein a percentage of cells negative for i) and positive for v) and vi) that is greater than 2% indicates that the sample is positive for biomarker 4.

22. The method according to claim 21, wherein the combination of positive biomarkers 1, 2, 3 and 4 has a sensitivity of at least 70% and a specificity of at least 75%.

23. The method according to claim 1, wherein the flow cytometry analysis comprises excluding from the data analysis those cells that have a diameter less than approximately 5 pm and greater than approximately 30 pm.

24. The method according to claim 1, wherein the flow cytometry analysis comprises excluding from the data analysis those cells that are dead cells and cell clusters of more than one.

25. The method according to claim 1, wherein the first labeled probe that binds to a biomarker expressed in a leukocyte population of sputum cells is a CD45 antibody or fragment thereof.

26. The method according to claim 1, wherein the second labeled probe is the granulocyte probe that binds to a biomarker expressed in a granulocyte population of sputum cells, being a CD66b antibody or fragment thereof.

27. The method according to claim 1, wherein the second labeled probe is the T cell probe that binds to a biomarker expressed on a T cell population from sputum cells, being a CD3 antibody or fragment thereof.

28. The method according to claim 1, wherein the second labeled probe is the B cell probe that binds to a biomarker expressed in a population of B cells from sputum cells, being a CD19 antibody or fragment thereof.

29. The method according to claim 1, wherein the second labeled probe is a combination of the granulocyte probe, the T cell probe, and the B lymphocyte probe.

30. The method according to claim 29, wherein the granulocyte probe is a CD66b antibody or fragment thereof, the T cell probe is a CD3 antibody or fragment thereof, and the B cell probe is a CD19 antibody or fragment thereof.

31. The method according to claim 1, wherein the third labeled probe that binds to a biomarker in a sputum cell macrophage population is a CD206 antibody or fragment thereof.

32. The method according to claim 1, wherein the fourth labeled probe that binds to a disease-related cell in the sputum sample is a tetra(4-carboxyphenyl)porphyrin (TCPP). nzRnnn / ιζηζπ / γα 33. The method according to claim 1, wherein the fifth labeled probe that binds to a biomarker expressed in a population of epithelial cells of sputum cells is a panCytokeratin antibody or fragment thereof.

34. The method according to claim 1, wherein the sixth labeled probe that binds to a cell surface biomarker expressed in a population of epithelial cells from sputum cells is an EpCam antibody or fragment thereof.

35. The method according to claim 1, wherein the disease-related cells are lung cancer cells or tumor-associated immune cells.

36. The method according to claim 1, wherein the lung disease is selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer.

37. The method according to claim 1, wherein the sputum cells are fixed or unfixed.

38. The method according to claim 1, wherein the data comprising cell-based cytometric data based on an average fluorescent signature of any of the labeled probes i) to vi) produce a sputum sample signature.

39. The method according to claim 38, wherein the signature of the sputum sample identifies the lung disease.

40. The method according to claim 39, wherein the lung disease is selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer.

41. The method according to claim 39, wherein the sputum sample signature is compared with a database of control (non-disease) sputum sample signatures and lung disease sample signatures to identify lung disease.

42. A first reactive composition for flow cytometry phenotyping of sputum cells from a sputum sample of a subject, to identify one or more biomarkers within the cell population that are associated with a probability of lung disease, wherein the reactive composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome; and antibodies conjugated to the fluorochrome or fragments thereof, directed against selected cell markers of ii) EpCAM, and / or panCytokeratin, iii) CD45, CD206, CD3, CD19, CD66b, or any combination thereof.

43. A second reagent composition for flow cytometry phenotyping of sputum cells from a sputum sample of a subject, to identify one or more biomarkers within the cell population that are associated with a probability of lung disease, wherein the reagent composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome and antibodies conjugated to the fluorochrome or fragments thereof, directed against the following cell markers: ii) EpCAM and / or panCytokeratin, and iii) CD45.

44. A third reactive composition for flow cytometry phenotyping of nzRnnn / Lznz / q / Yi cells from a sputum sample of a subject, to identify one or more biomarkers within the cell population that are associated with a probability of lung disease, wherein the reactive composition comprises: i) a tetra(4-carboxyphenyl)porphyrin (TCPP) fluorochrome; and antibodies conjugated to the fluorochrome or fragments thereof, directed against one or more of the following cell markers: CD45, CD206, CD3, CD19 and CD66b.

45. A method for predicting the probability of lung disease in a subject, said method comprising the steps of: labeling an ex vivo sputum sample with i) a labeled probe that binds to a disease-related cell in the sputum sample, and ii) one or more fluorochrome-conjugated probes directed against a sputum cell marker; and analyzing the labeled sputum sample by flow cytometry to obtain data comprising per-cell cytometric data, based on a mean fluorescent signature of any of the labeled probes i) to ii); and from the per-cell data detecting the probability of lung disease in a subject based on a profile of the presence or absence of i) and ii) in the labeled per-cell data.

46. ​​The method according to claim 45, wherein the data comprising cell-based cytometric data based on an average fluorescent signature of any of i) to ii) produce a sputum sample signature.

47. The method according to claim 46, wherein the signature of the sputum sample identifies the lung disease.

48. The method according to claim 47, wherein the lung disease is selected from the group consisting of asthma, CORD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-versus-host disease, and lung cancer.

49. The method according to claim 46, wherein the sputum sample signature is compared with a database of control (non-disease) sputum sample signatures and lung disease sample signatures to identify lung disease.

50. The method according to claim 45, wherein the labeled probe that binds to the disease-related cell in the sputum sample is a tetra(4-carboxyphenyl)porphyrin (TCPP).