Patent Application
20210102957
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Note that the following discussion refers to a number of publications by author(s) and year of publication, and that due to recent publication dates certain publications are not to be considered as prior art vis-a-vis the present invention. Discussion of such publications herein is given for more complete background and is not to be construed as an admission that such publications are prior art for patentability determination purposes.
Low-dose computed tomography (LDCT) is the current standard of care for screening for lung cancer as a method of early diagnosis, particularly in high-risk populations defined by the U.S. Centers for Medicare and Medicaid Services (CMS) as individuals who are 55-to-75-years-of-age, who smoked the equivalent of one pack of cigarettes each day for 30 years and who have not quit smoking with 15 years. While 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 trial of lung cancer screening to date. The LCST showed a false positive rate of 3.8% for LDCT in the high-risk population it studied, leading to many unnecessary, often invasive and potentially harmful follow-up procedures in patients who test positive by LDCT but who do not have lung cancer. There is thus a pressing need to improve the specificity of the LDCT and thereby lowering its false positive rate. One approach toward addressing this need is the development of additional assays with a high specificity for lung cancer that can be used as an adjunct to LDCT. The highly fluorescent tetra (4-carboxyphenyl) porphyrin (TCPP) selectively binds to cancer cells compared to normal cells, and is thus uniquely suited for the development of a diagnostic label that can distinguish cancer cells from surrounding background cells. The standard of care for screening individuals at high risk for lung cancer consists of annual imaging of the chest using LDCT (1). Although extremely sensitive, LDCT has a high false positive rate leading to multiple reflex diagnostic procedures with associated risks for patients who ultimately test negative for cancer. Risks include additional high-dose radiation exposure, and complications and morbidity from invasive procedures such as thoracentesis, bronchoscopy, and core needle biopsy. The risk of adverse events and the added financial burden associated with these procedures is significant, resulting in a clear medical need for safer and less invasive reflex testing after positive LDCT results (2). Alternative testing methods would ideally complement the high sensitivity of LDCT by increasing the specificity, lowering the false positive rate and improving the positive predictive value of screening with a reasonably priced adjunct test.
Minimally invasive techniques in the form of liquid biopsies have been proposed for reflex lung cancer testing following positive LDCT results. Using a liquid biopsy, circulating tumor cells (CTCs) and free tumor nucleic acids can be collected from the patient's peripheral blood sample. The CTCs and nucleic acids are tested using molecular techniques such as next-generation sequencing (NGS) for the presence of cancer-associated gene mutations that could predict the presence of cancer and how the patient's tumor might respond to a specific targeted therapy (3). While these technologies can identify mutations in an estimated 50-75% of lung cancers (4,5), LDCT-positive patients whose tumors lack such specific gene abnormalities will have negative results from a liquid biopsy. In addition, CTCs are rare (as low as 1 cell per 109 normal cells) and tumor nucleic acid concentrations are often below the limit of detection of most clinically available molecular testing methods (6). Thus, liquid biopsies have the potential to provide valuable treatment information about a patient's tumor genome but are better utilized at a later stage in the lung cancer diagnostic algorithm than tests aimed at early cancer diagnosis.
Liquid cytology testing of bronchial washings provides a sampling of potentially malignant cells for pathology review using the conventional sputum smear. The bronchoscopy procedures used to retrieve cells from a patient's airway are less invasive than a core needle lung tissue biopsy. However, there is still risk for adverse events such as hemorrhage (7). In addition, associated health care costs, particularly if performed on an inpatient basis, can be significant. Given that only a small minority (i.e., less than 4%) of LDCT-positive patients will be found to actually have lung cancer, there remains a medical need for alternative, economical, more accessible sources of malignant cells from the lung to provide diagnostic material.
Pathologists have performed routine cytological examination of sputum for decades as a non-invasive, rapid and specific detection method for lung cancer. In conventional sputum cytology, samples are stained and screened microscopically for malignant cells. However, conventional sputum cytology suffers from low (˜65%) sensitivity (8). Various methods to enhance sensitivity of sputum analysis have been attempted, including KRAS mutation testing. While KRAS testing can be both sensitive and specific if a patient's tumor is in fact KRAS mutated, only 15-20% of lung cancers actually harbor KRAS gene mutations. Thus, KRAS mutation-negative tumor cells will not be detected by this technique (9). An alternative DNA-based approach, referred to as automated sputum cytometry, utilizes special staining and computer-assisted image analysis to assess nuclear DNA characteristics of sputum epithelial cells for malignancy-associated changes. While this technique is somewhat more sensitive than conventional cytology, its specificity is only ˜50% (10).
One embodiment of the present invention provides for a method of predicting the likelihood of lung disease in a subject, the 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 a biomarker expressed on a white blood cell population of sputum cells; ii) a second labeled probe is selected from the group consisting of: a granulocyte probe that binds a biomarker expressed on a granulocyte cell population of sputum cells, a T-cell probe that binds a biomarker expressed on a T-cell cell population of sputum cells, a B-cell probe that binds a biomarker expressed on a B-cell cell population of sputum cells, or any combination thereof; iii) a third labeled probe that binds a biomarker on a macrophage cell 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 on an epithelial cell population of sputum cells; and vi) a sixth labeled probe that binds to a cell surface biomarker expressed on an epithelial cell population of sputum cells. The labelled sputum sample is analyzed, for example, flow cytometrically analyzed to obtain data comprising per cell cytometric data based upon a mean fluorescent signature of any of the i)-vi) labeled probes. The per cell data is detected to determine the likelihood of lung disease in a subject based upon a profile of a presence or absence of labeled probes in the per cell labelled data. The data obtained can be further analyzed to identify the presence or absence of a biomarker in a sputum sample. For example, the 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 vs. hose disease and lung cancer. Further, the sputum cells that are labelled may be fixed or non-fixed.
The data collected from the labelled sputum sample can be characterized by the populations of cells and biomarkers therefrom identified. For example, a ratio of the sputum cells in the data collected from the labelled sputum sample is determined that are negative for i) as compared to the sputum cells that are positive for i) to identify a biomarker 1. In one example, a ratio of less than 2 indicates the sputum sample is positive for biomarker 1. In one embodiment, the positive biomarker 1 has a sensitivity of at least about 80% and a specificity of at least 50% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarker 1. Wherein 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 the sputum sample is positive for biomarker 2. In one embodiment, the positive biomarker 2 has a sensitivity of at least 90% and a specificity of at least 50% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarker 2. Wherein 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, a biomarker 3 is identified when the sputum cells are positive for i), iii) and display FITC autofluorescence. For example, a percentage of sputum cells positive for i), iii) and display FITC autofluorescence that is greater than 0.03% indicates the sputum sample is positive for biomarker 3. In one embodiment the positive biomarker 3 has a sensitivity of at least 60% and a specificity of at least 70% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarker 3. Wherein 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, a biomarker 4 is identified when the sputum cells are negative for i) and positive for v) and vi) to identify a biomarker 4. For example, the percentage of cells negative for i) and positive for v) and vi) of more than 2% indicates the sample is positive for biomarker 4. In one embodiment, the positive biomarker 4 has a sensitivity of at least 70% and a specificity of at least 70% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarker 3. Wherein 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 embodiment more than one biomarker can be combined such as a combination of the positive biomarker 1 and the positive biomarker 2 to produce have a sensitivity of at least 80% and a specificity of at least 80% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarker 1 and 2. Further, the combination of positive biomarkers 1, 2, and 3 to produce a sensitivity of at least 80% and a specificity of at least 80% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarkers 1-3. Further still, the positive biomarkers 1-4 produce a sensitivity of at least 70% and a specificity of at least 75% to distinguish a lung cancer (c) sputum sample from a high risk (HR) sputum sample with the application of biomarkers 1-4. Wherein 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 embodiment, the flow cytometric analysis may include one or more of the following: excluding from data analysis those cells that have a diameter of less than about 5 μm and greater than about 30 μm, those cells that are dead cells and cell clumps of more than one.
In another embodiment, the first labeled probe that binds a biomarker expressed on a white blood cell population of sputum cells may be a CD45 antibody or fragment thereof.
In another embodiment, the second labeled probe is one or more of the following added either individually or in combination to the sputum sample: the granulocyte probe that binds a biomarker expressed on a granulocyte cell population of sputum cells and may be selected from a CD66b antibody or fragment thereof, the T-cell probe that binds a biomarker expressed on a T-cell cell population of sputum cells is a CD3 antibody or fragment thereof, the B-cell probe that binds a biomarker expressed on a B-cell cell population of sputum cells is a CD19 antibody or fragment thereof.
In another embodiment, the third labeled probe that binds a biomarker on a macrophage cell population of sputum cells is a CD206 antibody or fragment thereof.
In yet another embodiment, the fourth labeled probe that binds to a disease related cell in the sputum sample is a tetra (4-carboxyphenyl) porphyrin (TCPP).
In yet another embodiment, the fifth labeled probe that binds to a biomarker expressed on an epithelial cell population of sputum cells is a panCytokeratin antibody or fragment thereof.
In a further embodiment, the sixth labeled probe that binds to a cell surface biomarker expressed on an epithelial cell population of sputum cells is an EpCam antibody or fragment thereof.
The data collected may comprise per cell cytometric data based upon a mean fluorescent signature of any of the i)-vi) labeled probes to produce a sputum sample signature. The sputum sample signature identifies the health of the lung and/or lung disease. The lung disease may be selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft vs. hose disease and lung cancer. Further still, the sputum sample signature is compared to a database of control sputum sample signatures (non-diseased) and lung disease sample signatures to identify lung disease. In some embodiments of the present invention, results are classified using a trained algorithm. Trained algorithms of the present invention include algorithms that have been developed using a reference set of known sputum samples from subject at high risk of developing the disease, sputum samples for subjects confirmed to have the disease and sputum samples from subjects identified as normal (not having the disease or at high risk of developing the disease). Algorithms suitable for categorization of samples include but are not limited to k-nearest neighbor algorithms, concept vector algorithms, naive bayesian algorithms, neural network algorithms, hidden markov model algorithms, genetic algorithms, and mutual information feature selection algorithms or any combination thereof. In some cases, trained algorithms of an embodiment of the present invention may incorporate data other than sputum sample signatures or per cell cytometric data or mean fluorescent signature such as diagnosis by cytologists or pathologists or information about the medical history of the subject. In a programmed computer, the data is input to a trained algorithm to generate a classification of the sputum sample as high probability, intermediate probability or low probability of having the lung disease and electronically outputting a report that identifies said classification of said sputum sample for the lung disease.
One embodiment of the present invention provides for a first reagent composition for flow cytometric phenotyping of sputum cells from a sputum sample of a subject to identify one or more biomarkers within the population of cells that are associated with a likelihood of lung disease, wherein the reagent composition comprises: i) a tetra (4-carboxyphenyl) porphyrin (TCPP) fluorochrome; and a fluorochrome-conjugated antibodies directed against cell's markers selected from; ii) EpCAM, and/or panCytokeratin, and iii) CD45, CD206, CD3, CD19, CD66b or any combination thereof.
Another embodiment of the present invention provides for a second reagent composition for flow cytometric phenotyping of sputum cells from a sputum sample of a subject to identify one or more biomarkers within the population of cells that are associated with a likelihood of lung disease, wherein the reagent composition comprises: i) a tetra (4-carboxyphenyl) porphyrin (TCPP) fluorochrome and fluorochrome-conjugated antibodies directed against the following cell's markers; ii) EpCAM and/or panCytokeratin, and iii) CD45.
Another embodiment of the present invention provides for a third reagent composition for flow cytometric phenotyping of sputum cells from a sputum sample of a subject to identify one or more biomarkers within the population of cells that are associated with a likelihood of lung disease, wherein the reagent composition comprises: i) a tetra (4-carboxyphenyl) porphyrin (TCPP) fluorochrome; and fluorochrome-conjugated antibodies directed against one or more of the following cell's markers; CD45, CD206, CD3, CD19, and CD66b.
Yet another embodiment provides for a method of predicting the likelihood 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's markers. The labelled sputum sample is flow cytometrically analyzed to obtain data comprising per cell cytometric data based upon a mean fluorescent signature of any of the i)-ii) labeled probes. From the per cell data detecting the likelihood of lung disease in a subject based upon a profile of a presence or absence of i) and ii) in the per cell labelled data. The data comprising per cell cytometric data can be based upon a mean fluorescent signature of any of the i)-ii) produces a sputum sample signature. In one embodiment, the sputum sample signature identifies the lung disease for example, the lung disease is selected from the group consisting of asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft vs. hose disease and lung cancer. Further still, the sputum sample signature is compared to a database of control sputum sample signatures (non-diseased) and lung disease sample signatures to identify the lung disease from the labelled sputum sample. In one embodiment, the labeled probe that binds to the disease related cell in the sputum sample is a tetra (4-carboxyphenyl) porphyrin (TCPP).
Further scope of applicability of the present invention will be set forth in part in the detailed description to follow, taken in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art upon examination of the following, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.
The accompanying drawings, which are incorporated into and form a part of the specification, 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 only for the purpose of illustrating one or more embodiments of the invention and are not to be construed as limiting the invention. In the drawings:
Furthermore, the following terms shall have the definitions set out below. It is understood that in the event a specific term is not defined herein below, that term shall have a meaning within its typical use within context by those of ordinary skill in the art.
It is to be noted that as used herein and in the appended claims, the singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise.
The term “calibrate” means setting the sensitivity of the machine against the control reagents.
The term “compensation” means samples are compared against controls to determine background.
The term “fractionate” or “fractionated” means selecting a subset of events to further analyze. One example of fractionating is with “gates” to exclude/include data during analysis.
The term “gate” means boundaries are placed around populations of cells with common characteristics, usually forward scatter, side scatter, and marker expression, to investigate and to quantify these populations of interest.
The term “probe” means a ligand, peptide, antibody or fragment thereof that has affinity for and binds to a biomarker on the surface of a cell or particle or to a marker within the cell or particle.
Porphyrins concentrate in all types of cancer cells. In addition, certain porphyrins are naturally fluorescent, with a characteristic photon emission profile. A porphyrin composition is described herein for use in a high-throughput assay (especially a flow cytometric assay) to distinguish fluorescence of porphyrins that label cancer cells or cells associated with a disease state from surrounding background cells (11).
Referring now to
In flow cytometry each cell or particle is hydrodynamically focused to a photocell. Each cell or particle passes through one or more beams of light as the cell/particle passes through the photocell. 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 discrete frequencies (coherent light). They are available at different wavelengths ranging from ultraviolet to far red and have a variable range of power levels (photon output/time). Light that is scattered in the forward direction, typically up to 20° offset from the laser beam's axis, is collected by a photomultiplier tube (PMT) or photodiode known as the forward-scatter (FSC) channel. The FSC equates roughly to the cell's/particle's size. Typically, larger cells refract more light than smaller cells. Light measured at an approximately 90° angle to the excitation line is called side scatter (SSC). The SSC channel provides information about the relative complexity (for example, granularity and internal structures) of a cell or particle. Both FSC and SSC are unique for every cell or particle, and a combination of the two may be used to roughly differentiate cell types in a heterogeneous sample such as blood, sputum, for example, but not limited thereto. An event is identified when a cell or particle passes through the laser beam and a signal is generated as a function of time. For FSC and SSC, the time that the cell or particle spends in the laser is measured as the width “W” of the event 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 pulse generated by the cell or particle passing the interrogation point of a laser beam in the cytometer. As used herein cell and particle may each be recorded as an event when passing through the beam of light in the photocell.
Referring now to
Specialized airway epithelium cells and glandular cells lining the bronchi secrete mucus. The mucus produced deep within the lung can contain a large variety of cells that are recycled from the lung tissue, including epithelial cells, alveolar cells, macrophages and other hematopoietic (blood) cells (17). The mucus also contains non-cellular material, which is especially noticeable in lungs from people who smoke, live in highly polluted areas or are exposed to other airway allergens (such as pollens). When mucus originating from within the lung is coughed up, it is called sputum. Sputum is often mixed with saliva produced in the oral cavity that contains many BECs (or cheek cells), which adds another cellular component to an already complex tissue sample (see
As opposed to microscopy, flow cytometry can provide for multidimensional information and/or more exacting information regarding cell populations from sputum, because it allows the elimination of debris and cells that are not of interest based on size, granularity and/or fluorescence markers, thereby enriching the sample for cells of interest. To enrich for red fluorescent cells (RFC)s in sputum cell analysis, the first step is to approximate the size (diameter) of RFCs; anything smaller or larger is excluded. RFCs are the cells with the highest TCPP uptake, i.e., cancer cells and cancer-associated macrophages, because both cell types take up more TCPP than any other cell type (18-22). The size of lung cancer cells may vary and depend on the type of cancer but is not likely to significantly differ from 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 diameter of alveolar macrophages is measured to be 21 μm. Of special interest are the macrophages and lymphocytes, since specific subpopulations of each of these cell types are known to alter their function when associated with cancers (23-26). However, RBC (6-8 μm) and anything smaller (debris), as well as BECs (65 μm) and anything larger can be excluded from further analysis.
Referring now to
In one embodiment, debris and BECs are excluded from a population of cells to be further analyzed. Standard-size beads (5, 10, 20 and 50 μm) are used in a light-scatter profile (where the forward side scatter (FSC) represents cell size and side scatter (SSC) represents granularity;
BECs demonstrate very high SSC characteristics that made them distinct from WBCs and HCC15 cells (
Sub-Fractionation of Hematopoietic Cells into Discrete 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 (CD45positive cells) can be distinguished from other cells, including normal lung epithelial cells and potential lung cancer cells (CD45negative cells). To identify the specific hematopoietic subpopulations in sputum we used additional probes, for example, antibodies directed at granulocytes (CD66b), macrophages (HLA-DR, CD11b, CD11c, CD206) and lymphocytes (CD3 and CD19). Table 2 identifies exemplary probes and fluorophores.
Referring now to
The remaining CD45positiveCD66bnegative 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 markers for macrophages confirmed that the majority of the cell population in
Referring now to
In another embodiment, combining the CD3/CD19 markers with the CD66b marker allows identification of potential lymphocyte contamination in the macrophage/monocyte population (the CD66bnegative/CD3negative/CD19negative subset of cells) in those samples that happen to harbor a discernible lymphocyte population (28-30). Gating the CD3positive/CD19positive/CD66bpositive population of cells out of the CD45positive population of cells analyzed for TCPP signal is yet another method for improving signal related to the TCPP label.
Referring now to
Another component of the flow cytometry-based sputum analysis for early cancer detection is the CyPath® labeling of cancer cells. We analyzed sputum samples obtained from high-risk patients (presumably without lung cancer) and spiked the sample with approximately 3% HCC15 cancer cells. For this experiment, which is outlined in
Referring to
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Five sputum samples in a small pilot experiment were analyzed: One sample from a healthy volunteer, three samples from high risk patients without cancer and one sample from a lung cancer patient. The analysis was performed as described in
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A first sputum sample from the subject is treated with a CD45 probe conjugated to a fluorophore and a cocktail of CD66B, CD3, CD19 conjugated to a fluorophore and CD206 conjugated to a fluorophore and TCPP (tube #6).
Referring not to
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In each sample, 9 populations can be identified as illustrated in
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Volunteers were recruited to provide a three-day sputum sample. Three distinct study cohorts were included: 1) individuals at high risk for developing lung cancer, but presumably cancer free, 2) high risk individuals diagnosed with lung cancer and 3) healthy individuals (22 years and older) not diagnosed with cancer and not at high risk for developing lung cancer. To be eligible for the high-risk cohort, subjects had to be heavy cigarette smokers defined as 30 pack years and between 55 and 75 years of age (13). (Examples of 30-pack-years smoking are: 1 pack per day per year for 30 years, 2 packs per day per year for 15 years, etc.) For the healthy cohort, subjects had to have smoked for ≤5 pack years and/or have quit 15 years earlier and be 22 years of age or older. Other exclusion criteria (applicable to all cohorts) were the presence of severe obstructive lung disease, uncontrolled asthma, angina with minimal exertion, pregnancy or working in the mining industry.
All study participants were trained in the use of the Acapella® assist device (Smiths Medical, St. Paul, Minn.), in accordance with the manufacturer's instructions. The Acapella® device is an FDA-approved, hand-held device that helps to thin and mobilize mucous secretions from deep within the lung. Subjects were instructed to use the device and expel the sputum sample into a sterile collection cup. Subjects repeated this procedure at home to collect the second- and third-day sputum samples. Subjects were instructed to store their specimen cup in a cool, dark place or in a refrigerator and to return it to the site of initial collection within 1 day after collection was complete. Completed specimen cups were packed with frozen transport ice packs and sent overnight to be analyzed. The cell viability in the 3-day collection samples received (n=38) was on average 64.3% (SD: 25.6%; range: 23.6-100%), not including buccal epithelial cells (BECs or cheek cells), which are all dead (14).
Sputum plugs were separated from contaminating saliva using a cotton swab (15,16). When plug selection was not possible, the whole sample was processed. The sputum was mixed with pre-warmed 0.1% dithiothreitol (DTT) at a 1:4 ratio with sputum plug weight (w/w) and 0.5% N-acetyl-L-cysteine (NAC) at a ratio of 1:1. The mixture was then rocked for 15 minutes at room temperature. GIBCO® Hank's Balanced Salt Solution (HBSS; ThermoFisher Scientific, Waltham, Mass.) was added (4 times the volume of the sputum/DTT/NAC mixture), and the resulting cell suspension was rocked 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×g for 10 minutes. After decanting the supernatant, the cell pellet was re-suspended in 1 mL of HBSS. The total cell count was determined with a Neubauer hemocytometer using the trypan blue exclusion method to determine cell viability.
The same cotton swabs used to transfer the sputum plugs for processing were used to transfer sputum cells onto one slide. Using an additional slide, the sputum sample was smeared between two slides to cover a large part of both slides (16). The slides were air-dried and stained with Wright-Giemsa. One or both slides were read and the number of macrophages counted by a pathologist.
Two 7-mL vials of peripheral blood were obtained from healthy volunteers. The majority of blood was used to obtain white blood cells (WBC) by lysing the red blood cells (RBC) with BD Pharm Lyse™ (BD Biosciences, San Jose, Calif.). The remainder was used for a source of RBC.
BECs were harvested from oral mucosa of healthy volunteers by scraping the inner cheek with a cell scraper. BECs-containing saliva was processed using the same protocol as that for the dissociation of sputum cells.
HCC15 lung cancer cells (ATCC, Manassas, Va.) were grown in RPMI 1640, supplemented with 10% Fetal Bovine Serum and 1% penicillin/streptomycin, in a 5% CO2 incubator set to 37° C.
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-CD11b-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 purchased from BioLegend (San Diego, Calif.), whereas anti-CD11c, anti-CD66b, anti-CD206, anti-HLA-DR and their respective isotype controls were purchased from BD Biosciences. Additional antibodies are listed in TABLE 2.
Tetra (4-carboxyphenyl) porphyrin (TCPP) was purchased from Frontier Scientific (Logan, Utah) and the 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 on sputum cells and in some cases on blood cells (CD3 and CD19) to determine the optimal staining concentration to reflect the largest differential in fluorescence intensity compared to their isotype controls. The optimal concentration of TCPP and EpCAM was titrated on sputum cells and HCC15 cells. The other staining reagents and beads were used as per the manufacturer's recommendation.
Referring now to
The cells in the sputum sample can be fractionated based upon the presence of live cells (LC) and dead cells (DC) and whether there are single cells (SC) or double cells captured as an event as described herein.
Samples of single-cell suspension of dissociated sputum samples in
Referring to
In one embodiment, samples were analyzed using a BD LSR-II flow cytometer (BD Biosciences), equipped with 4 lasers (404 nm, 488 nm, 561 nm and 633 nm). Cell sorting of whole sputum, CD45positive CD206positive, CD45positive CD66bpositive, or CD45positive CD66negative subpopulations were performed on a BD FACSAria cell sorter (BD Biosciences). Post-collection data analysis was performed with FlowJo software (Tree Star, Inc. Ashland, Oreg.).
Whole sputum samples were prepared using the sputum dissociation method described above. Cytospins were prepared with 1 and 2.5×105 cells per slide, using a Cytopro 7620 (Wescor, Logan, Utah) Hettich 32A (Rotofix, Beverly, Mass.) cytocentrifuges. Slides were stained with either Wright or Wright-Giemsa staining, following manufacturer's protocols. Images were produced at room temperature on a Nikon Eclipse Ti or an Olympus BX40 microscope. The Nikon microscope is equipped with an UPlanApo20×/0.7 objective and a DS-Ri2 camera, the Olympus microscope with a PLAPO60×/1.4 objective and a SD100 camera. NIS-Elements Advanced Research (Nikon) and CellSens Standard (Olympus) were used to secure the images.
Macrophages have traditionally been used to verify sputum sample adequacy. The guideline of the Papanicolaou Society of Cytopathology for evaluating sputum samples by cytology states that: “No numerical cut point for number of macrophages is consistently reported in the literature, but an adequate specimen should have numerous easily identifiable cells of this type” (31). HLA-DR and CD11 b (or CD11c), together with CD14 and CD206 have been shown to be useful markers for the flow-cytometric identification of different subsets of macrophages and monocytes within the lung (32,33). CD206 is a marker specific for alveolar macrophages that are long-lived cells, which have populated the lung during embryonic development (34). The CD206positive macrophages, although of hematopoietic origin, cannot be found in the blood circulation. This population of macrophages is specific for the lung tissue (34) and is thus a good candidate to serve as a measure to verify sample adequacy.
Samples are prepared for analysis as described in
Sputum samples are weighed and based upon the weight, dissociation reagents are added as follows: 1 volume of 0.5% NAC solution to sample and 4 volumes of 0.10% DTT solution to sample. The sample is vortexed and agitated at room temperature. Thereafter 4 volume of 1× Hank's Balanced Salt Solution (HBSS) based on the total current volume (sputum+NAC+DTT solution). The sample is filtered and then centrifuged at 800×g for 10 minutes. The supernatant is aspirated and the pellet resuspend with HBSS according to the sample size (for example, small (≤3 g) sample, add 250 μl HBSS, medium (>3-≤8 g) sample, add 760 μl HBSS, large (>8 g) sample, add 1460 μl HBSS). A 1:10 dilution is used for cell yield determination.
0.5% N-acetyl-L-cysteine (NAC) solution: Add 0.85 g of sodium citrate dihydrate to 45 mL of ddH2O, 500 μL of 3 M NaOH, 0.25 g NAC and stir until dissolved. pH solution to between about 7.0-8.0 and adjust volume to 50 mL with ddH20
0.10% dithiothreitol (DTT) solution: Add 0.10 g DTT to 100 mL of ddH2O and stir until dissolved. Split solution in 10 mL aliquots and freeze/store in −20° C. until use.
1 mg/mL CyPath TCPP stock solution as follows: Add 25 mL Isopropanol and 0.2 g Sodium Bicarbonate to 25 mL ddH2O and stir until dissolved. Adjust pH of solution to between about 9 to 10 if necessary. Add 0.05 g TCPP, protect solution from light, and stir until dissolved.
Table 4 indicates μl of cells to be aliquoted into tubes for counting and antibody labeling.
Antibody/FVS Labeling
Sputum cells are aliquoted according to Table 4 to the reagents identified in Table 5 which are added to set up experimental and control tubes for the labeling of dissociated sputum cells.
1:66.7
1:16.7
1:66.7
Table 6, and Table 7 and Table 9: Samples for bead size, compensation of the flow cytometer, isotype control, sputum background and treated sputum are prepared as described.
Tubes #1-#7 are incubated in the dark for 35 min. After antibody incubation, each tube is filled with cold HBSS, and the supernatant is spun down at 800×g for 10 minutes at 4° C. The supernatant is discarded and the pellet is resuspended as follows: To tubes #1-#3 add 0.5 mL cold HBSS to tubes and store on ice, at 4° C., until data acquisition by flow cytometry. To tube #4 and #5, add 2 mL cold 1% PFA fixative. To tubes #6 and #7 add 10 mL cold 1% PFA fixative. Incubate tubes for 1 hour on ice, covered with foil. After fixative incubation, fill each tube with cold HBSS. Spin down cells at 1600×g for 10 minutes at 4° C. Aspirate supernatant as much as possible, but without disturbing the pellet. Re-suspend the pellet in the residual fluid. Re-suspend tube #4 and #5 in 0.2 mL cold HBSS and store with tubes #1-#3 on ice, at 4° C., until data acquisition by flow cytometry. For tubes #6 and #7, add ice-cold HBSS according to the following formula:
Final volume (mL) of each tube=0.15*[Total Cell/106] (formula 1)
For cell #, obtain cell count from a 1:40 diluted cell suspension with trypan blue. Add 10 μL of the 1:40 dilution to a hemocytometer and count the cells in all four large quadrants. Accurate cell count constitutes 25-60 cells per quadrant.
Place tubes #6 and #7 overnight on ice, at 4° C., until ready for TCPP labeling on day 2.
CyPath Assay TCPP working solution is made as a 20 μg/mL TCPP solution (1:50 of stock), using cold HBSS and is protected from light. Obtain 1 tube with A549 cells to be used as unstained control for FVS and TCPP labeling (Tube #8). Obtain 1 tube with A549 cells to be used as compensation tube for FVS labeling (Tube #9). Obtain 1 tube with A549 cells to be used as compensation tube for TCPP labeling (Tube #10). Obtain 1 tube with A549 cells to be used as compensation tube for PanCK labeling (Tube #11)
Add Cypath Assay TCPP working solution volume according to Table 10
Incubate the samples with TCPP for about 1 hour, fill tubes #6, #7 and 10 with cold HBSS and centrifuge at 1000×g for 15 minutes at 4 C. Aspirate supernatant without disturbing pellet. For tubes #6, #7 and #10 wash the pellet with cold HBSS and repeat centrifuge and wash steps. For tubes #6, #7 and #10 re-suspend the pellet in the residual fluid and add 300 μL cold HBSS to tube #10, if the total cell count is <20×106 cells total, 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).
Flow Cytometric Data Acquisition
The flow cytometry acquisition rate of 10,000 events/sec or lower is preferred with the following settings: Parameters used on the LSRII include: Threshold, FSC voltage, SSC voltage, BV510 voltage wherein this voltage should be checked on ALL cells, including the BECs, PE voltage, FITC voltage, PE-TxRed voltage, and APC voltage. For optimization of the assay using equivalent flow cytometers, one of ordinary skill in the art will know preferred settings to achieve same or similar results.
Summary of fluorescence intensity values that determine the population gates:
It should be noted that the referenced settings are specific for the LSRII instrument and may vary for other flow cytometers but will be apparent to one of ordinary skill in the rt how to compensate for the different instruments to produce comparable ranges.
While the above examples are exemplary for lung cancer detection, other diseases and conditions of the lung can be detected and/or monitored over time with a system and method as disclosed herein. For example, when the subject is suspected of developing or prone to have an exacerbation of symptoms associated with lung diseases such as asthma, COPD, influenza, chronic bronchitis, tuberculosis, cystic fibrosis, pneumonia, graft-vs.-host disease, sputum may be analyzed for the alterations in the distribution of cell populations as compared to a database of control (non-diseased) and disease sample profiles.
Note that in the specification and claims, “about” or “approximately” means within twenty percent (20%) of the numerical amount cited. All computer software disclosed herein may be embodied on any computer-readable medium (including combinations of mediums), including without limitation CD-ROMs, DVD-ROMs, hard drives (local or network storage device), USB keys, other removable drives, ROM and firmware.
In at least one embodiment, and as readily understood by one of ordinary skill in the art, the apparatus, according to the invention, will include a general- or specific-purpose computer or distributed system programmed with computer software implementing the steps described above, which computer software may be 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 over the Internet and/or one or more intranets) in a variety of hardware implementations. For example, data processing can be performed by an appropriately programmed microprocessor, computing cloud, Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like, in conjunction with appropriate memory, network, and bus elements. The multidimensional data recorded from the cells and particles analyzed as they move through the flow cytometer are recorded and permit analysis and fractionation of the cell populations based upon the multidimensional optical properties.
Although the invention has been described in detail with particular reference to these embodiments, other embodiments can achieve the same results. Variations and modifications of the present invention will be obvious to those skilled in the art and it is intended to cover in the appended claims all such modifications and equivalents. The entire disclosures of all references, applications, patents, and publications cited above are hereby incorporated by reference.
This application is a continuation application of International Patent Application No. PCT/US2019/027550, titled “System and Method for Determining Lung Health”, filed on Apr. 15, 2019, which claims priority to and the benefit of the filing of U.S. Provisional Patent Application No. 62/657,584, titled “System and Method for Determining Lung Health”, filed Apr. 13, 2018, and the specification and claims thereof are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62657584 | Apr 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2019/027550 | Apr 2019 | US |
| Child | 17069272 | US |
