CN110023760B

CN110023760B - Diagnostic tool for evaluating immunity and providing colorectal cancer morbidity information by using distribution difference of immune cells in peripheral blood of colorectal cancer patients and normal people

Info

Publication number: CN110023760B
Application number: CN201880004489.6A
Authority: CN
Inventors: 李宗均; 崔重范
Original assignee: Individual
Current assignee: Individual
Priority date: 2017-06-02
Filing date: 2018-01-11
Publication date: 2023-04-14
Anticipated expiration: 2038-01-11
Also published as: KR102018006B1; CN110023760A; WO2018221820A1; KR20190088403A

Abstract

The invention relates to a method for evaluating the immunity of peripheral blood by utilizing the distribution difference of immune cells in peripheral blood of a colorectal cancer patient and a normal person and providing colorectal cancer morbidity information by utilizing the data, which is characterized by comprising the following steps: (A) Analyzing the cell size and the degree of folding in peripheral blood immune cells to classify lymphocytes (lymphocytes), monocytes (monocytes) and granulocytes (granulocytes); (B) The stage of staining the markers of the above three types of immune cells with at least one antibody combination and analyzing the distribution of immune cells in the peripheral blood of cancer patients and normal persons; (C) A stage of discriminating a significant marker result value between cancer patients and normal persons showing a combination of statistical significance and difference in order to determine the onset of cancer in both cancer patients and normal persons; and (D) a stage of diagnosing the onset of colorectal cancer by detecting the immunity in the blood of the cell using the above-mentioned marker without calculating natural killer cells using a flow cytometer, a cell counter or the like.

Description

Diagnostic tool for evaluating immunity and providing colorectal cancer morbidity information by using difference of distribution of immune cells in peripheral blood of colorectal cancer patients and normal people

Technical Field

The present invention relates to a method for diagnosing colorectal cancer patients by measuring and diagnosing peripheral blood immunity of cancer patients including colorectal cancer and normal persons, finding markers showing a significant difference between the two groups, generating and applying a binary logistic regression algorithm based on the combination of the above markers, and providing criteria for immunotherapy.

Background

Immunity (Immunity) is closely related to all processes such as onset and metastasis of cancer. In the actual research literature to date, there is a gap in the extent to which it is suggested that the immunity of most Cancer patients is significantly reduced (Low) or Impaired (Impaired) (Olivera J. Finn, cancer Immunology, N Engl J Med, 2008. These findings have led to the development of new therapeutic approaches to cancer therapy (surgery and anti-cancer therapy) and the development of new concept "immunotherapy". Subsequently, various forms of immunotherapy have been developed and applied to clinical patients, and some of the therapies have been developed to achieve visual effects.

The so-called immunity is not determined by one factor but is effected by integration of various factors such as various cells and proteins (Immune cells and proteins) forming the Immune system (Immune system). Therefore, the immunity (Personal immunity) of individuals is different and changes occur at any moment. This means that tailor-made therapies (Tailored personal immunity) are required for immunotherapy based on the individual's different immunity. The general Treatment process (Treatment or Therapy) refers to the process of diagnosing a disease (Diagnosis) and then determining the corresponding Therapy. The same is true of immunotherapy, which requires immunodiagnostics (immunodiagnosis) to provide a corresponding treatment, but most current immunotherapies are performed without a correct diagnosis. This is because, as mentioned above, the immunity of each individual is very complex and lacks the methodology and basis for evaluating and diagnosing immunity.

At the level of diagnosis and prevention of cancer, the most common methods are based on imaging diagnostics monitoring and histological analysis. For preventing cancer, comprehensive precise physical examination is very important, but the process needs time and cost, and the busy modern daily life is considered, so that the comprehensive precise physical examination is difficult to popularize. Therefore, various diagnostic studies are currently being conducted in an attempt to find more convenient and accurate preventive methods. The most common method is to predict Cancer by finding out a blood tumor marker (Cancer marker) by ELISA or the like. Blood sampling is simple and ELISA is not complicated, and thus is one of the commonly used methods.

Meanwhile, in addition to proteins, blood samples (Blood samples) contain almost the majority of Immune cell populations (Immune cells) and their subtypes (Immune cell subtypes). Such immune cell analysis is mainly accomplished using a Flow cytometer (Flow cytometer). The desired immune Cell Marker (Marker) can be fluorescently stained using a flow cytometer, and additionally, immune cells can be analyzed very simply and with reproducibility by using flow cytometric Fluorescence Sorting (FACS).

Disclosure of Invention

(problem to be solved)

The object of the present invention is to find a marker that a colorectal cancer patient has a difference from a normal person among immune cells constituting Peripheral blood immunity (Peripheral blood immunity) in order to diagnose cancer immunity.

In addition, the present invention is directed to provide a method of diagnosing immune activity in an individual by designing a statistical algorithm by combining the markers and simply and easily measuring the cell activity of natural killer cells (nkcells) without counting the number of nkcells using a cell counter, thereby providing a corresponding method of immunotherapy.

(means for solving the problems)

The invention relates to a method for evaluating the immunity of peripheral blood by using the distribution difference of immune cells in peripheral blood of a colorectal cancer patient and a normal person and providing colorectal cancer onset information by using the data, which is characterized by comprising the following steps: (A) Analyzing the cell size and the degree of folding in peripheral blood immune cells to classify lymphocytes (lymphocytes), monocytes (monocytes) and granulocytes (granulocytes); (B) Staining the markers of the above three types of immune cells with at least one antibody combination, and analyzing the distribution of immune cells in the peripheral blood of cancer patients and normal persons; (C) A stage of discriminating a significant marker result value between cancer patients and normal persons showing a combination of statistical significance and difference in order to determine the onset of cancer in both cancer patients and normal persons; and (D) detecting the immunity of the cells in blood by using the above-mentioned markers without calculating natural killer cells by using a flow cytometer, a cell counter, or the like, and diagnosing the onset of colorectal cancer.

The invention also relates to a recording medium capable of being interpreted by a computer provided with a computer program for implementing the method.

In addition, the invention also relates to a diagnostic tool for providing information on the onset of colorectal cancer according to the method.

(Effect of the invention)

According to the present invention, a marker of interest in immune cells contained in a Blood sample (Blood sample) can be selected, analyzed by flow cytometry, and cancer immunity can be diagnosed by an algorithm. For preventive purposes, the above-described examination method is not only very simple and quick, but also saves considerable costs, as compared to receiving a precise physical examination, but also offers sufficiently high accuracy. Meanwhile, compared with an ELISA method for measuring a known blood tumor marker (Cancer biomarker), reproducibility (Reproducibility) and Stability (Stability) are improved, so that the method has the advantage of improving the inspection reliability.

Drawings

FIG. 1 is a schematic diagram illustrating cell phenotype for analysis of NK cells according to one of the preferred embodiments of the present invention.

FIG. 2 is a schematic diagram illustrating cell phenotype for analysis of Th1/Th2 according to one of the preferred embodiments of the present invention.

FIG. 3 is a schematic diagram illustrating a cell phenotype for the analysis of Myeloid Derived Suppressor Cells (MDSCs) in accordance with one preferred embodiment of the present invention.

FIG. 4 is a schematic diagram illustrating cell phenotype for the analysis of regulatory T cells (Tregs) according to one of the preferred embodiments of the present invention.

FIG. 5 is a schematic diagram illustrating cell phenotype for the analysis of cytotoxic T Cells (CTLs) according to one of the preferred embodiments of the present invention.

FIG. 6 is a schematic diagram illustrating cell phenotype for the analysis of depleted T cells (ETc) in accordance with one of the preferred embodiments of the present invention.

FIG. 7 is a schematic diagram illustrating cell phenotype for analysis of Immune Checkpoints (ICP) according to one of the preferred embodiments of the invention.

FIG. 8 is a schematic diagram illustrating CD3- γ δ TCR + cell phenotype for analysis of Gamma delta T cells (GDT) in accordance with one of the preferred embodiments of the present invention.

Fig. 9 is a Receiver operating characteristic curve (Receiver operating characteristic curve) generated by using an E value according to one preferred embodiment of the present invention.

FIG. 10 is a schematic diagram of a model for classifying cancer immunity into 3 stages, E1, E2, E3, etc., using 2E-value cut values according to one of the preferred embodiments of the present invention.

Detailed Description

The following detailed description of the invention, which is to be read in connection with the accompanying drawings, is a specific embodiment of the invention. The following examples are set forth in sufficient detail to enable those skilled in the art to practice the invention. It is to be understood that the various embodiments of the invention, although different, are not necessarily mutually exclusive. For example, a particular embodiment described herein may be embodied as another embodiment without departing from the spirit and principles of the invention. Alternatively, in the separately described embodiments, individual components, positions and order of stages or arrangement may be varied without departing from the spirit and principles of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims, along with the full scope of equivalents to which such claims are entitled, if appropriate.

The present invention will be described in detail below with reference to the accompanying drawings of preferred embodiments according to the present invention in order that the present invention may be easily implemented by workers having ordinary knowledge in the field of the present invention.

The cell analysis in the present invention basically uses a Flow cytometer (Flow cytometer), and the following 6 kinds of immune cells (WBC, lymphocytes, neutrophiles, monocytes, basophiles, eosinophiles) including leucocyte subtypes (WBCS) are used with a full-Automatic hematology analyzer (Automatic hematology analyzer).

The present invention is based on the markers exhibited in the following groups 9 of immune cells in blood of patients and normal persons before colorectal cancer surgery, and shows immune cell phenotypes corresponding to the respective markers in terms of degree of distribution (%) and cell number (cells/. Mu.L) and Ratio thereof (Ratio). The operation can be classified into the following categories according to the function of immune cells and the analysis method:

1) Natural Killer Cell (NKC)

2)Th1Th2(TH)

3) Myeloid Derived Suppressor Cells (MDSCs)

4) Regulatory T cells (Tregs)

5) Cytotoxic T Cells (CTLs)

6) Depleted T cell (ETc)

7) Immune Check Point (ICP)

8) Gamma delta T cell (GDT)

9) Leukocyte subtype (WBCS)

The 9 immune Cell populations have numerous Cell membrane (Cell surface) and cytoplasm (intracellular cellular) receptors or signaling substances, and these cellular substances can be regarded as Cell markers, determining the inherent functions of the immune cells. Meanwhile, among the cell markers, the function and the degree of appearance of some markers are very closely related to the onset and progression of cancer. In practice, one of the major current immunotherapies is also the inhibition of cancer cell proliferation by amplifying or blocking signal transduction using markers for immune cells (kathleen m. Major, combination cancer immunity and new immunity targets,2015, nat Rev Drug disorders).

The present invention focuses on this point, and divides the main immune cell markers into meaningful groups, and arranges and screens them. At the same time, it was tested whether the selected markers also showed significant development differences between the colorectal cancer patient group and the normal human group in the actual peripheral blood immunity unit, and judged whether they could be used as diagnostic markers. Statistically, meaningful markers have actually been identified among patients, but Sensitivity (Sensitivity) and Specificity (Specificity) of such markers are difficult to distinguish adequately colorectal cancer patients from normal persons by the individual markers. Therefore, the present invention develops an algorithm to combine meaningful markers for both groups of colorectal cancer patients and normal persons to provide meaningful information and mathematically derive a combination to divide the difference between the two groups. The mathematical model used in the present invention is based on the principle of binary logistic regression. By the invention, various regression analysis models can be obtained by using various combined markers, and more than 80% of sensitivity and specificity of colorectal cancer patients and normal people can be displayed, so that the peripheral blood immunity can be evaluated as a technology for early diagnosis of cancers. In clinical diagnosis, sensitivity and specificity of 80% are usually required to use a certain diagnostic method.

The following examples and data derived by the examples are used to describe in detail a diagnostic method which can diagnose colorectal cancer patients and normal persons with sensitivity and specificity up to 80% using various combinations of markers.

Flow cytometric analysis of peripheral blood immune cells by fluorescent staining

After collecting 5cc (5 ml) of peripheral blood from 98 colorectal cancer patients and 132 normal patients, respectively, heparin-EDTA anticoagulation tubes (Heparin-EDTA tubes) were placed to prevent blood coagulation, and the tubes were transported to an analysis room and immediately analyzed.

To analyze each immune cell marker, the immune cells were classified into 8 types as follows, and 8 Polystyrene tubes (12x75mm Polystyrene tube) were prepared. The following antibody combinations were used for each of the 8 tubes, and the amounts of the antibodies used were as shown in tables 1 to 8. Tables 1 to 8 show labeled antibodies for analyzing Natural Killer Cells (NKCs), th1Th2 (Th), myeloid suppressor cells (MDSCs), regulatory T cells (Tregs), cytotoxic T Cells (CTLs), depleted T cells (ETc), immune Checkpoints (ICP), and Gamma delta T cells (GDT), respectively, all antibodies are in a state in which fluorescent substances (fluorescent substances dye) are attached to mouse anti-human IgG (mouse anti-human IgGs), and the types of fluorescent substances used in the present invention include 7 types such as FITC, baioboboboboboboblepori 488, PE-Cy5, PE-Cy7, perCP, and APC. In the upper item of each table, "Channel" indicates the number of detector channels of the flow cytometer, "tape-dye" indicates the kind of fluorescent substance attached to the antibody, "Marker" indicates the kind of Marker, and "Marker location" indicates the position of each Marker. However, there are differences in the experimental methods described below depending on the type of the label.

[ Table 1]

Natural Killer Cell (NKC)

[ Table 2]

Th1Th2(TH)

[ Table 3]

Myeloid Derived Suppressor Cells (MDSCs)

Channel

Fluorescent substance

Marking

Marking position

Suppliers of goods

Directory number

Batch numbering

Amount/times

FL1

FITC

CD3

Cell surface

BD

555339

6125658

0.5

FL1

FITC

CD19

Cell surface

BD

555412

5097663

0.5

FL1

FITC

CD56

Cell surface

BD

340410

6141554

2.5

FL2

PE

CD11b

Cell surface

BD

555388

4314750

0.1

FL3

PE-Cy5

HLA-DR

Cell surface

BD

555813

6132725

2.5

FL4

APC

CD33

Cell surface

BD

551378

4288542

0.1

[ Table 4]

Channel

Fluorescent substance

Marking

Marking the position

Suppliers of goods

Directory number

Batch numbering

Amount/times

FL1

FITC

CD4

Cell surface

BD

555346

5097644

0.5

FL2

PE

CD25

Cell surface

BD

555432

6040885

2.5

FL3

PE-Cy7

CD152

Inside the cell

BD

555854

5142830

2.5

FL4

APC

CD279

Cell surface

BD

558694

6154800

2.5

[ Table 5]

Cytotoxic T Cells (CTLs)

Channel

Fluorescent substance

Marking

Marking position

Suppliers of goods

Directory number

Batch numbering

Amount/times

FL1

FITC

CD3

Cell surface

BD

555339

6125658

0.5

FL2

PE

CD25

Cell surface

BD

555432

6040885

2.5

FL3

PE-Cy7

CD152

Inside the cell

BD

555854

5142830

2.5

FL4

APC

CD279

Cell surface

BD

558694

6154800

2.5

[ Table 6]

Depleted T cell (ETc)

[ Table 7]

Immune Check Point (ICP)

[ Table 8]

Gamma delta T cell (GDT)

The basic dyeing method adopts the following procedures.

(1) A microcentrifuge tube (microcentrifuge tube) was used to remove the amount of each antibody tested (volume/test) to 1.8mL using a micropipette (micro pipette). The combined amount of each antibody was 10. Mu.L per test. Meanwhile, the preparation contents may be different depending on the position (marker location) of the target marker to be stained in the antibody combination, on the cell membrane (cell surface) or the cytoplasm (cell interior), and generally, after only the antibodies stained on the cell membrane (cell interior) are combined and stained and the cell is fixed, the cytoplasm (cell interior) marker is prepared and stained (hybridization).

For example, natural Killer Cells (NKC) whose labeled sites are on the cell membrane were analyzed and tested in 10 cases. mu.L of FITC mouse anti-human CD3 IgGs (0.5. Mu. L x 10 testes), 25. Mu.L of PE mouse anti-human CD56 IgGs (2.5. Mu. L x 10 testes), 25. Mu.L of PE-Cy7 mouse anti-human CD314 IgGs (2.5. Mu. L x 10 testes), 5. Mu.L of APC mouse anti-human CD158b IgG (0.5. Mu. L x 10 testes) were placed in a microcentrifuge tube, and a total of 60. Mu.L was prepared. Thereafter, 40. Mu.L of PBS (Phosphate-buffered saline) was put into the tube, and finally 100. Mu.L of the antibody combination was prepared.

As another example, when 10 antibodies were tested for the combination sites labeled on the cell membrane and cytoplasm, 2 antibody tubes were prepared.

First, 5. Mu.L of FITC mouse anti-human CD4 IgGs (0.5. Mu. L x 10 tests), 25. Mu.L of PE mouse anti-human CD25 IgGs (2.5. Mu. L x 10 tests), and 5. Mu.L of APC mouse anti-human CD279IgG (0.5. Mu. L x 10 tests) as antibodies for cell membrane staining were put into a first tube, respectively, and 35. Mu.L of the mixture was prepared. Thereafter, 40. Mu.L of PBS (Phosphate-buffered saline) was put into the tube, and finally 100. Mu.L of the antibody combination was prepared.

Then, in the second tube, 25. Mu.L (2.5. Mu. L x 10 tests) of cytoplasmic antibody PE-Cy5 mouse anti-human CD152IgGs was prepared, and then 75. Mu.L of PBS (phospholate-buffered saline) was put into the tube to finally prepare 100. Mu.L of the antibody combination.

In the same manner, staining antibodies against Natural Killer Cells (NKC), th1Th2 (Th), myeloid suppressor cells (MDSCs), depleted T cells (ETc), and Gamma delta T cells (GDT), which are composed of a labeled site on the cell membrane, and staining antibodies against regulatory T cells (Tregs), cytotoxic T Cells (CTLs), and Immune Checkpoints (ICP), which are composed of a labeled site on the cell membrane and cytoplasm in combination, were prepared for staining the cell membrane and cytoplasm. The volume ratio of each antibody is shown in "volume/test ratio" in tables 1 to 8, and antibody combinations can be prepared in the manner described above.

(2) After preparing all antibody combination tubes for staining, 50. Mu.L of whole blood (whole blood) was injected into each of 8 polystyrene tubes.

(3) In a previously prepared tube containing the antibody combination (antibody combination for cell membrane), 10. Mu.L of the antibody combination was taken into a test tube containing blood, and shaken with a micropipette. Thereafter, the resultant was dyed at room temperature for 20 minutes in a light-shielded state.

(4) After completion of cell staining, to remove Red Blood Cells (RBCs) in blood, 450 μ L of lysis solution (BD FACS lysis, DB Biosciences) was aliquoted into 8 polystyrene tubes, mixed with a vortex mixer for 20 minutes, and then reacted in a room for 20 minutes in a light-shielded state.

(5) After 2mL of PBS was injected into each tube, the tube was treated with a centrifuge at a gravitational acceleration (G force) of 250 for 5 minutes. Subsequently, the supernatant was removed, and the cells were washed with 2,mL of PBS by the same centrifuge.

(6) After staining with the cell membrane (cell surface) antibody alone, the cells were shaken with 200. Mu.L of PBS, and analyzed by a flow cytometer.

(7) When mixed antibodies of cell membrane (cell surface) and cytoplasm (cell interior) were stained, after completion of the above (5) process, 40. Mu.L of distilled water containing 0.05% saponin (saponin) and 10. Mu.L of the prepared antibody for cytoplasm staining were put into a tube at the same time, shaken, and then stained 2 times at room temperature for 20 minutes in a light-shielded state. Subsequently, according to the procedure (5), 200. Mu.L of PBS was put into the tube and analyzed by a flow cytometer.

The distribution (%) can be derived from the result obtained by the flow cytometry analysis, and the cell number (cells/. Mu.L) based on the distribution can be calculated by multiplying the distribution by the differential leukocyte cell count (differential) obtained by a fully automatic hemocytometer.

For example, when CD3-CD56+ natural killer cells =15%, the number of cells can be calculated to be 3000x 0.15=450cells/μ L using the number of lymphocytes of 3000cells/μ L.

Through the above experimental process, the analysis results of each marker of immune cells by flow cytometry are shown in fig. 1 to 8, and the results of analyzing colorectal cancer patients and normal persons are shown in fig. 9 to 18. For reference, fig. 1 to 8 and fig. 10 are rotated 90 degrees in a counterclockwise direction, and therefore, the following description is made with reference to drawings rotated 90 degrees in a clockwise direction.

FIG. 1 provides (1) CD3-CD56+,

(2) CD3+ CD56+, (3); CD314+ CD158b-in CD3-CD56+ cells (natural killer cells), (4) natural killer cells CD314-CD158b +, (5) natural killer cells CD314-CD158b +, (6); CD158b + in CD3+ CD56- (T cells), etc.

Specifically, the upper left chart of fig. 1 is a chart in which blood is analyzed by flow cytometry, and then cells are classified into lymphocytes (lymphocytes), monocytes (monocytes) and granulocytes (granulocytes) according to the cell size and the degree of folding of peripheral blood immune cells by using FSC-H (relative cell size) as the X axis and SSC-H (degree of folding of cells) as the Y axis. Among them, lymphocytes (Lymphocytes) at the central portion of the graph were analyzed, and the result value was obtained by staining with an antibody attached to the central portion. On the upper right of the graph, the X-axis shows whether CD2 is stained or not, the Y-axis shows whether CD56 is stained or not, and the left side can be regarded as CD3+, CD56+ below, and CD56+ above, with the cross-shaped solid line inside the graph as the center. At this time, CD3-CD56+, CD3+ CD56-, CD3-CD 56-are designated as Q1, Q2, Q3, and Q4, respectively. In the lower left of the graph, the cells in the Q1 region were further sorted by antibody according to the staining pattern of the CD314 and CD158 markers, and the display pattern was as described above. In the lower right of the graph, the cells in the upper right Q3 region of the graph were classified by the staining with antibodies according to CD314 and CD158 markers, and the display pattern was as described above.

FIG. 2 shows Th1/Th2 assay according to the preferred embodiment of the present invention provides 4 cell phenotypes, such as (1) Th1, (2) Th2, (3) Th17, (4) Th1/Th 2.

Specifically, FIG. 2 shows the results of analysis using the cells of the lymphocyte fraction as shown in FIG. 1, and the results of analysis are shown after classifying the cells according to the staining conditions of the markers CD4, CD183, CD194, and CD196 by placing the antibodies in sequence or simultaneously. The X-axis and Y-axis of the graph show the presence or absence of reaction with the added antibody with data, and the solid line within the graph subdivides each region. The detailed method is as described in FIG. 1. On the other hand, the distribution degrees of Th1, th2, and Th17 can be calculated by the following equations:

[ equation 1]

Th2＝R5(9.73％)×Q7(79.2％)×1/100＝7.71％

Th1＝R4(12.8％)×Q4(64.9％)×1/100＝8.31％

Th17＝R5(9.73％)×Q5(20.8％)×1/100＝2.02％

Th1/Th2＝8.31％/7.71％＝1.08

FIG. 3 illustrates a preferred embodiment of the present invention for analyzing the cell phenotype of Myeloid Derived Suppressor Cells (MDSCs).

Specifically, the method of displaying the graph is the same as that described with reference to fig. 1. As shown in the graph, the cell group showing negative (negative) for both HLA-DR and the Lineage series (CD 3CD19CD 56) antibody, and the cell showing CD33+ and CD11b + in the Q4 region were dominant. MDSCs can be calculated using equation 2 below.

[ equation 2]

MDSCs＝[Livecells(92.4％)×Q4(63.9％)×1/100]×R10(96.4％)×1/100＝56.91％

FIG. 4 provides 3 cell phenotypes for regulatory T cell (Tregs) analysis according to a preferred embodiment of the present invention (1) ++CD279 +, (2) +CD25+, (3) +CD152+, etc. The contents of the diagrams shown in fig. 4 to 8 are the same as those in fig. 1, and thus, the detailed description thereof will be omitted.

FIG. 5 provides 2 cell phenotypes for cytotoxic T Cell (CTLs) analysis according to a preferred embodiment of the present invention, (1) ++in CTLs, (2) +CD279 +in CTLs, etc.

FIG. 6 is an assay of depleted T cells (ETc) providing CD279+ TIGIT + in CTLs cell phenotype according to a preferred embodiment of the present invention.

FIG. 7 provides 9 cell phenotypes for Immune Checkpoint (ICP) analysis according to a preferred embodiment of the present invention, such as (1) CD3+ CD366+, (2) CD3-CD366+, (3) lymphocyte CD366+, (4) CD3+ CD272+, (5) CD3-CD272+, (6) lymphocyte CD272+, (7) CD3+ CD223+, (8) CD3-CD223+, (9) lymphocyte CD223+, etc.

FIG. 8 provides CD3- γ δ TCR + cell phenotype for Gamma delta T cell (GDT) analysis according to a preferred embodiment of the present invention.

On the other hand, table 9 shows the mean distribution degree and cell number of NK cells in peripheral blood of patients with colorectal cancer and normal persons. Table 10 shows the mean distribution and cell number of TH cells in peripheral blood of patients with colorectal cancer and normal persons. Table 11 shows the mean distribution of MDSCs cells in peripheral blood and the number of cells in patients with colorectal cancer and in normal persons. Table 12 shows the mean distribution and cell number of Tregs cells in peripheral blood of colorectal cancer patients and normal persons. Table 13 shows the mean distribution of CTLs in peripheral blood and the number of cells in patients with colorectal cancer and in normal persons. Table 14 shows the mean degree of distribution and number of peripheral blood ETc cells in colorectal cancer patients and normal persons. Table 15 shows the mean distribution of peripheral blood ICP cells and cell number in colorectal cancer patients and normal persons. Table 16 shows the mean distribution and cell number of peripheral blood GDT cells in colorectal cancer patients and normal persons. Table 17 shows the mean distribution and cell number of WBCS cells in peripheral blood of colorectal cancer patients and normal persons. Table 18 shows the Ratio of peripheral blood immune cells (Ratio) of colorectal cancer patients and normal persons.

In the upper items of the tables, "N" is the number of the experimental group, "Mean" is the average, "s.d." is the standard deviation, "s.e." is the standard error, "95% Mean" is the average calculated by subtracting the result values of 2.5% of the two ends of the cluster in order to reduce the error rate and using the value of 95%, and "Median" is the Median.

[ Table 9]

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[ Table 10]

[ Table 11]

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[ Table 12]

[ Table 13]

[ Table 14]

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[ Table 15]

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[ Table 16]

[ Table 17]

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[ Table 18]

Colorectal cancer diagnosis method based on binary logistic regression model

As shown in tables 9 to 18, in the peripheral blood immune cell analysis of 132 normal persons and 98 patients as a whole, it was found that there was a marker of immune cells showing group differences at the significance level P value <0.05, and the parts marked with oblique bold letters in the tables were considered to be significant immune cell markers. The mean difference between the two groups was analyzed by the statistical program SPSS. The parent follows normal distribution and meets equal dispersion conditions, so the statistical difference of the mean value adopts the student's T-test of the T test.

Therefore, the distribution map and the cell number of immune cells in peripheral blood can be determined, and the colorectal cancer patients and normal people can be accurately classified by using the immune cell markers specific to the cancer immunity, and the cancer can be diagnosed by the immunity examination. In order to be able to carry out a reliable cancer diagnosis in this way, an formulated binary logistic regression model is used in cancer diagnosis in order to be able to represent the differences in the peripheral blood immunity between colorectal cancer patients and normal persons with high certainty.

Colorectal cancer patients and normal persons were set as dependent variables to complete the algorithm, and assigned values of 1 and 0, respectively. The independent variables were obtained by labeling immune cells as shown in tables 9 to 18. Meanwhile, the items that most easily distinguish the two groups of 132 normal persons and 98 colorectal cancer patients were also screened.

[ Table 19]

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Table 19 above identifies the coefficients (B) and constants (Constant) in the regression analysis model using the 23 immune cell markers, where B in the upper table entry is the estimated value of B, corresponding to the coefficient values in the regression equation model, s.e is the standard error value for the estimated value of B, and Wald is the squared value of "standard error value for estimated value of B/estimated value of B". That is, (B/s.e.) 2 denotes a statistic that verifies the statistical significance of each independent variable. Df is the degree of freedom, sig represents the probability of significance, representing the significance of each item in the model. Exp (B) represents eB which is a natural logarithm of the B value, and is a statistic indicating that the probability of belonging to the internal value 1 group is a multiple of the probability of belonging to the internal value 0 group when each argument is increased by 1.

The logistic regression analysis formula using the above Constant (Constant) and coefficient (B) is shown below.

Logit (P) =23.317-0.403 (CD 4 +%) -0.468 (CD 3+ CD8 +%) +0.961 (CD 4+ CD279 +%) +0.646 (CD 4+ CD25 +%) +0.001 (CD 4+ CD152 +%) -0.093 (CTLs CD279 +%) +0.131 (CTLs CD152 +%) +0.623 (CD 3+ CD272 +%) +0.479 (CD 3+ CD223 +) -0.221 (lymphocytes%) -0.174 (neutrophils%) -1.056 (NLR) +4.576 +%) -0. (CTLs/Treg) -0.011 (Natural killer cell CD314+ CD158 b-%) +0.739 (Natural killer cell CD314-CD158b +%) -0.140 (T cell CD314 +) +0.450 (Th 1%) +0.074 (Th 2%) -7.516 (TH 1/TH 2) -0.001 (MDSCs cells/. Mu.L) +0.495 (monocyte%) -1.866 (eosinophil%) -13.906 (basophil%)

However, in the case of making an algorithm with 23 items, sensitivity and specificity may tend to decline when blinding cancer patients and normal persons in an expected manner. Moreover, the logistic regression analysis function is composed of 23 items, and the number is too large, so the actual result value may be largely determined by the item with higher weight in the 23 items; this may be a factor that hinders rapid inspection since more variables of relevant data are required. Therefore, the following logistic regression composed of 11 factors is more preferably used.

Logit(P)＝-17.461+0.039(CD3％)+0.141(NK％)+0.320(CD4CD279％)+0.196(CD4+CD25+％)-0.105(CD4+CD152+％)+0.157(CD3+CD366+％)+0.243(CD3+CD272+％)+0.006(CD3+CD223+％)+0.350(CD158b+CD314-CD3-CD56+％)+0.143(Th2％)+0.001(MDSCs cells/μL)

If the result values are obtained by the flow cytometric analysis of the immune cells as described above, each result value is multiplied by a coefficient value, plus a constant value, and then a Logit (P) value is found. The objective here is to use regression analysis formula to diagnose cancer by replacing 1 and 0 for colorectal cancer patients and normal persons. Therefore, it is necessary to input an arbitrary new test result value, to see what kind of value is observed between 1 and 0, and to predict whether it is a colorectal cancer patient or a normal person. Therefore, a conversion may be performed so as to converge the value Logit (P) of the linear formula model to values of 1 and 0 with an exponential function. At this time, the prediction formula for distinguishing colorectal cancer patients (value of appearance 1) or normal persons (value of appearance 0) using the exponential function is formula 3 shown below.

[ equation 3]

y＝e ^P /l-e ^P

As shown in equation 3, the predicted Y values of colorectal cancer patients and normal persons are obtained by using eP as a numerator and 1-eP as a denominator, and the probability Y value is named as an E value in the present invention.

As shown in figure 9 of the drawings,

plotting the receiver operating characteristic curve for the E value, the AUC value is found to be 0.98. It can be considered as being able to diagnose colorectal cancer on the basis of peripheral blood immunity.

[ Table 20]

Cut value	Sensitivity (%)	Specificity (%)	Joden index
				0.098	100	78.4	1.784
0.548	88.1	96.9	1.851
				0.684	83.1	100	1.831

Table 20 shows the values of sensitivity, specificity and Youden index (Youden index) obtained from the above E values. As shown in Table 20, the cleavage values of the E value result values with respect to sensitivity and specificity can be arbitrarily adjusted. When the cleavage value was set to 0.098, the sensitivity was 100% and the specificity was 78.4%. When the cleavage value was 0.684, the sensitivity was 83.1% and the specificity was 100%. This means that if the E values are <0.098, all can be said to be normal persons, and likewise, if the E values are 0.684-straw, all can be said to be colorectal cancer patients.

That is, an exponential function in which the values of cancer patients and normal persons converge to 1 and 0 is designed using the values of the linear equation, and after an optimal cut-off value between 0 and 1 is found using the york index in the ROC curve, a binary logistic regression algorithm that can diagnose the onset of cancer is derived based on the optimal cut-off value.

Meanwhile, after the numerical value is calculated by using the retrospective design algorithm, random blind test (blid test) is carried out on a new group of normal people and cancer patients, and the immunity of peripheral blood is prospectively evaluated, so that the cancer is diagnosed.

As shown in fig. 10, in the logistic regression analysis diagnosis using the peripheral blood immunity, it was found that the cut value was not necessarily set to 1. Therefore, according to the present invention, the cut value of the E value is divided into three segments based on 0.098 and 0.684. Based on the E value results, cancer immunity can be diagnosed in three stages. The segment with the E value less than or equal to 0.098 is a normal person and is named as 'E1'; sections with E values less than or equal to 0.684 of 0.098 yarn-dyed fabrics are high risk groups and are named as 'E2'; the 0.684 pieces of values were tied to colorectal cancer patients, designated "E3".

The invention takes the colorectal cancer patients before operation as examples, and utilizes the peripheral blood immunity to diagnose normal people (E1), cancer high risk people (E2) and colorectal cancer patients (E3). But is not limited to colorectal cancer in methodology, but may of course be applicable to other cancers. Meanwhile, by using a regression model obtained by 23 or 11 immune cell markers, the sensitivity and specificity of the markers can be improved to realize the maximization of the cancer diagnosis applicability after more markers are developed in the future. In addition to the 23 or 11 items disclosed in the present invention, regression models can be derived by new combinations and are suitable for cancer diagnosis.

While specific elements of the present invention, including specific components, as well as limited embodiments and illustrations, have been described above, it is to be understood that the above description is merely illustrative of the present invention and that the present invention is not limited to the disclosed embodiments, but may be modified and varied by those skilled in the art to which the present invention pertains.

Therefore, the spirit of the present invention is not limited to the above embodiments, and not only the terms of the patent claims, but also modifications equivalent or equivalent to the terms of the patent claims are included in the scope of the present invention.

Claims

1. A computer-readable medium having recorded thereon a computer program for executing the method, the method comprising:

(A) Analyzing the cell size and the degree of folding in peripheral blood immune cells to classify lymphocytes (lymphocytes), monocytes (monocytes) and granulocytes (granulocytes);

(B) Staining the markers of the above three types of immune cells with at least one antibody combination, and analyzing the distribution of immune cells in the peripheral blood of cancer patients and normal persons;

(C) A stage of discriminating a significant marker result value between cancer patients and normal persons showing a combination of statistical significance and difference in order to determine the onset of cancer in both cancer patients and normal persons; and

(D) Detecting immunity in blood of the unit by using the above marker, diagnosing the onset of colorectal cancer,

the above-mentioned stage (D) includes,

(D-1) a stage of designing a linear equation having at least one meaningful combination value of markers capable of distinguishing cancer patients from normal persons as coefficients;

(D-2) designing an exponential function in which the values of the cancer patient and the normal person converge to 1 and 0, respectively, using the values of the linear equation, and deriving a binary logistic regression algorithm that can diagnose the onset of cancer based on the optimal cut value (CutValue) between 0 and 1, which is obtained by using the Youden index (Youden index) in the ROC curve; and

(D-3) performing a random blind test (blid test) on a new group of normal and cancer patients, prospectively evaluating the immunity of peripheral blood using the values calculated by the algorithm designed retrospectively, thereby diagnosing the stage of cancer,

in the stage (C), the P value is calculated by a statistical method based on a meaningful marker combination that can distinguish cancer patients from normal persons, and whether the P value satisfies a certain criterion or not is judged,

logit(P)=23.317-0.403(CD4 ⁺ %)-0.468(CD3 ⁺ CD8 ⁺ %)+0.961(CD4 ⁺ CD279 ⁺ %)+0.646(CD4 ⁺ CD25 ⁺ %)+0.001(CD4 ⁺ CD152 ⁺ %)-0.093(CD279 ⁺ % in CTLs)+0.131(CD152 ⁺ % in CTLs)+0.623(CD3 ⁺ CD272 ⁺ %)+0.479(CD3 ⁺ CD223 ⁺ )-0.221(Lymphocytes %)-0.174(Neutrophils%)-1.056(NLR)+4.576(CTLs/Treg)-0.011(NKcells CD314 ⁺ CD158b ^- %)+0.739(NK cellsCD314 ^- CD158b ⁺ %)-0.140(T cells CD314 ⁺ ) A linear equation of +0.450 (Th 1%) +0.074 (Th 2%) -7.516 (TH 1/TH 2) -0.001 (MDSCscells/. Mu.L) +0.495 (monocytes%) -1.866 (eosinophil%) -13.906 (basophil%).

2. The computer-readable medium of claim 1, wherein the immune cells are classified in stage (A)

1) Natural Killer Cell (NKC)

2)Th1Th2(TH)

3) Myeloid Derived Suppressor Cells (MDSCs)

4) Regulatory T cells (Tregs)

5) Cytotoxic T Cells (CTLs)

6) Depleted T cell (ETc)

7) Immune Check Point (ICP)

8) Gamma delta T cell (GDT)

9) Leukocyte subtype (WBCS)

9 broad categories were analyzed.

3. The computer-readable medium of claim 2, wherein during the stage (B), cell analysis of Natural Killer Cells (NKCs), th1Th2 (TH), myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), cytotoxic T Cells (CTLs), depleted T cells (ETc), immune Checkpoints (ICPs), and Gamma adelta T cells (GDT) is performed using a flow cytometer, and cell analysis of WBCs, lymphocytes, neutrophils, monocytes, basophils, and eosinophils included in a leukocyte subtype (WBCS) is performed using an automatic hemocytometer.

4. The computer-readable medium according to claim 2, wherein in stage (B), natural Killer Cells (NKC), th1Th2 (Th), myeloid-derived suppressor cells (MDSCs), depleted T cells (ETc), gamma deltaT cells (GDT) immune cells are fluorescently stained for their Cell membrane (Cell surface) markers;

for regulatory T cells (Tregs), cytotoxic T Cells (CTLs), immune Checkpoint (ICP) immune cells, cell surface (Cell surface) or intracellular (cellular) markers are fluorescently stained and analyzed using flow cytometry.

5. The computer-readable medium according to claim 1, wherein in the stage (B), the degree of distribution (%), the number of cells, and the ratio thereof of the phenotype of the immune cells corresponding to each marker are analyzed based on the markers developed by the immune cells.

6. The computer-readable medium of claim 1, wherein the values of P of Myeloid Derived Suppressor Cells (MDSCs) in MDSCs cells/μ L satisfy a certain criterion based on the average degree of distribution and the number of cells.

7. The computer readable medium of claim 1, wherein the P-value of a White Blood Cell Subtype (WBCS) in WBC cell/μ L, lymphocyte, neutrophil/μ L, neutrophil/lymphocyte, monocyte, eosinophil, basophil% satisfies a certain criterion based on the average distribution and the number of cells.

8. The computer-readable medium of claim 1, wherein the value of P satisfies a criterion in CD4/CD8, TH1/TH2 as a ratio of peripheral blood immune cells (ratio) between cancer patients and normal humans.

9. The computer-readable medium according to claim 1, wherein in the (D-3) stage, the sensitivity and the specificity are used as criteria, and after a numerical value is calculated by using a binary logistic regression algorithm when the sensitivity is at a set point, the numerical value is set as a 1 st cut value; when the specificity is a set value point, the algorithm is used for calculating a numerical value, and then the numerical value is set as a 2 nd cutting value; the group of values calculated by the algorithm below the 1 st cut-off value is normal, the group between the 1 st cut-off value and the 2 nd cut-off value is a high risk group for cancer, and a cancer patient is diagnosed when the value is above the 2 nd cut-off value.