WO2018221820A1 - Method for assessing immunity and providing information on whether or not the onset of cancer has begun by utilizing difference in immune cell distribution between peripheral blood of colorectal cancer patient and normal person, and diagnostic kit using same - Google Patents

Abstract

Provided according to the present invention is a method for assessing immunity of peripheral blood and providing information on whether or not the onset of colorectal cancer has begun by utilizing a difference in immune cell distribution between peripheral blood of colorectal cancer patients and normal persons, the method comprising the steps of: (A) analyzing peripheral blood immune cells for cell size and wrinkle degree to classify the cells into lymphocytes, monocytes, and granulocytes; (B) staining a labeling marker of the three kinds of immune cells with at least one antibody combination to analyze the distribution of immune cells in peripheral bloods of cancer patients and normal persons; (C) discriminating sets in which meaningful result values of the labeling marker are different between the two groups, cancer patients and normal persons, with statistical significance, so as to discriminate whether or not the onset of cancer has begun among cancer patients and normal persons; and (D) measuring immunity per unit of blood by means of the labeling marker to diagnose whether or not the onset of colorectal cancer has begun, without counting natural killer cells with a flow cytometer or a cell counter.

Description

Method for evaluating immunity and providing information on cancer development using differences in distribution of immune cells in peripheral blood of colorectal cancer patients and normal people, and diagnostic kit using the same

The onset was performed by assessing and evaluating peripheral blood immunity of cancer patients and colorectal cancer, including colorectal cancer, and finding markers that differ significantly between the two groups, and creating and applying a binary logistic regression algorithm based on a combination of these. To diagnose and provide criteria regarding immunotherapy.

Immunity is intimately involved in everything from cancer to cancer progression and progression and metastasis. Indeed, many of the literatures studied so far report that, although there are some differences, the immunity of most cancer patients is markedly low or impaired (Olivera J. Finn, Cancer Immunology, N Engl J Med, 2008: 358: 2704-15) These findings have led to the exploration of new treatments away from traditional cancer treatments (surgery and chemotherapy), which has led to the emergence of a new concept of treatment called immunotherapy. It became. Accordingly, various forms of immunotherapy are being researched and applied to clinical patients, and some immunotherapies have shown tangible results.

Immunity is not determined by one factor, but by the net effect of various factors, such as the immune cells and proteins that make up the immune system. Therefore, the personal immunity (Personal immunity) is inevitably different and will change from time to time. This means that individualized immunotherapy requires tailored personal immunotherapy based on different immunity. In general, treatment or therapy is a process in which diagnosis of a disease is preceded by a diagnosis and then a treatment is determined. Immune diagnosis is also possible before the immunotherapy (Immune diagnosis) can be treated accordingly, most of the immunotherapy is carried out without accurate immunodiagnosis. This is because, as mentioned above, the individual's immunity is so complex that no methodological criteria or evidence for assessing and diagnosing it have been established.

The most common method for diagnosing and preventing cancer is based on radiological monitoring and histological analysis. Although screening to prevent cancer is very important, it is a time-consuming and costly process that is difficult to take into account when considering the daily lives of busy modern people. Therefore, various diagnostic methods have been researched to find simpler and more accurate preventive methods. In general, the easiest way is to find cancer markers in the blood using a method such as ELISA to predict cancer. This is one of the many attempts because blood sampling is simple and ELISA is not difficult.

In addition, in blood samples, in addition to proteins, almost all immune cells and their subtypes are present. Analysis of such immune cells is mainly performed using a flow cytometer. Using a flow cytometer, fluorescent markers of desired immune cells can be fluorescently stained, and immunocyte analysis can be performed very simply and reproducibly using fluorescence-activated cell sorting (FACS).

An object of the present invention is to find markers that show differences in colorectal cancer patients and normal people among immune cells constituting peripheral blood immunity for diagnosing cancer immunity.

In addition, an object of the present invention is to establish a statistical algorithm through a combination of these and based on this individual cells by simply and easily measuring the cell activity of natural killer cells without counting the number of natural killer cells (NK cells) using a cell counter To diagnose the immune activity of the immune system is to provide.

According to the present invention, a method for evaluating the immunity of peripheral blood using the difference in the distribution of immune cells in peripheral blood of a colorectal cancer patient and a normal person and using the same to provide information on the presence or absence of colorectal cancer, (A) peripheral blood immunity Classifying lymphocytes, monocytes, and granulocytes by analyzing the cellular size and wrinkles of the cells; (B) analyzing the distribution of immune cells in peripheral blood of cancer patients and normal persons by staining the markers of the three types of immune cells with at least one antibody combination; (C) determining a combination of differences in cancer markers and normal persons with statistically significant result values of the marker markers in the two cancer patients and normal populations so as to determine whether cancer has occurred; And (D) diagnosing the presence of colorectal cancer by measuring immunity per unit blood without counting natural killer cells using a flow cytometer or a cell counter using the label marker.

According to the present invention, there is also provided a computer readable recording medium having recorded thereon a computer program for executing the method.

In addition, according to the present invention there is provided a diagnostic kit for providing information on the presence of colorectal cancer by the method.

According to the present invention, a significant marker may be selected from immune cells contained in a blood sample, and analyzed using a flow cytometer to diagnose cancer immunity through a regression analysis algorithm. This has the advantage of being very simple, quick to inspect, significant cost savings and sufficient accuracy compared to being overhauled in a preventive manner. In addition, there is an advantage that the reliability of the test can be improved by increasing reproducibility and stability, compared to an ELISA that measures and diagnoses a known tumor biomarker in blood.

1 is a view for explaining the cell phenotype for NKC analysis according to an embodiment of the present invention.

Figure 2 is a diagram for explaining the cell phenotype for Th1 / Th2 analysis according to an embodiment of the present invention.

Figure 3 is a view for explaining the cell phenotype for analysis of Myeloid Derived Stem Cells (MDSCs) according to an embodiment of the present invention.

Figure 4 is a view for explaining the cell phenotype for the analysis of Regulatory T cells (Tregs) according to an embodiment of the present invention.

5 is a diagram illustrating a cell phenotype for analysis of cytotoxic T cells (CTLs) according to an embodiment of the present invention.

Figure 6 is a view for explaining the CD279 + TIGIT + in CTLs cell phenotype for Exhausted T cells (ETc) analysis according to an embodiment of the present invention.

7 is a diagram illustrating a cell phenotype for Immune checkpoint (ICP) analysis according to a preferred embodiment of the present invention.

8 is a view for explaining the CD3-γδ TCR + cell phenotype for Gamma-delta T cells (GDT) analysis according to an embodiment of the present invention.

9 is a receiver operating characteristic curve made using the E score according to an embodiment of the present invention.

10 is a view showing a model for providing cancer immunity in three stages of E1 E2 E3 using two E score cut values according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION The following detailed description of the invention refers to the accompanying drawings, which are shown by way of illustration of specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different but need not be mutually exclusive. For example, certain embodiments described herein can be embodied in other embodiments without departing from the spirit and scope of the invention. In addition, it is to be understood that the location, order, or arrangement of individual components or steps in each disclosed embodiment may be changed without departing from the spirit and scope of the invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement the present invention.

Cell analysis in the present invention basically used a flow cytometer (Flow cytometer), the following six kinds of immune cells (WBC, Lymphocytes, Neutrophils, Monocytes, Basophils, Eosinophils) included in the white blood cell subtype (WBCS) Was analyzed using an automatic hematology analyzer.

The present invention is based on the marker markers expressed in the nine kinds of immune cell populations in the blood of patients and normal people before colorectal cancer surgery, the distribution of the immune cells for each marker marker (%) and the number of cells (cells) / μL) and its ratio (Ratio), which can be classified into the following categories by the function and analysis method of immune cells.

1) NK cells (NKC)

2) Th1Th2 (TH)

3) Myeloid Derived Stem Cells (MDSCs)

4) Regulatory T cells (Tregs)

5) Cytotoxic T cells (CTLs)

6) Exhausted T cells (ETc)

7) Immune checkpoint (ICP)

8) Gamma-delta T cells (GDT)

9) White blood cell subtype (WBCS)

These nine immune cell populations have a myriad of cell surface and intracellular receptors or signal transduction materials, which can be regarded as markers of cells, which are responsible for determining the function of immune cells. . In addition, some of the cell markers are closely related to the onset and progression of cancer in terms of their function and expression. Indeed, one of the main factors in current immunotherapy is to inhibit cancer cell proliferation by amplifying or blocking signal transduction through markers of immune cells (Katheleen M. Mahoney, Combination cancer immunotherapy and new immunomodulatory targets, 2015, Nat Rev Drug Discov).

In light of this, the present invention classifies and organizes major cellular immune markers into significant groups. In addition, it was directly tested whether the selected markers actually showed significant differences in expression levels between colorectal cancer patients and two normal groups even in peripheral blood immunity units and were useful as diagnostic markers. Although statistically significant markers were actually confirmed by the present patients, these markers were single items that showed sufficient sensitivity and specificity to diagnose or distinguish colon cancer patients from normal individuals. Did. In order to provide meaningful information for markers showing statistical significance in the two groups of colorectal cancer patients and normal people, the algorithm was developed. The mathematical model used in the present invention is based on binary logistic regression. Through the present invention, various regression analysis models were obtained by using various combinations of markers, and a technique for evaluating peripheral blood immunity and early diagnosis of cancer by exhibiting sensitivity and specificity of more than 80% in diagnosing colorectal cancer patients and normal people. It was confirmed that this is possible. In clinical diagnosis, it is usually required to have a sensitivity and specificity of 80% or more in order to be usefully accepted.

Hereinafter, a method for diagnosing a colorectal cancer patient and a normal person to have a sensitivity and specificity of 80% or more using various combinations of markers will be described in more detail based on Examples and data derived therefrom.

Through fluorescent staining of peripheral blood immune cells Flow cytometry

5 cc (5 ml) of peripheral blood were collected from 98 patients with colorectal cancer and 132 normal patients, respectively, in a Heparin-EDTA tube to prevent blood clotting and transported to the assay room at room temperature for immediate analysis. Proceeded.

In order to analyze each immune cell marker, the following eight immune cell categories were classified, and eight polystyrene tubes (12 × 75 mm polystyrene tubes) were prepared. In each of the eight tubes, the following antibody combinations were used, and the volume of each antibody is shown in Tables 1 to 8 below. Tables 1 to 8 show NK cells (NKC), Th1Th2 (TH), Myeloid Derived Stem Cells (MDSCs), Regulatory T cells (Tregs), Cytotoxic T cells (CTLs), Exhausted T cells (ETc), and Immune checkpoint ( ICP), an antibody for analyzing markers of Gamma-delta T cells (GDT), all of which have a fluorescent dye attached to anti-human mouse anti-human IgGs. The kinds of fluorescence used in the invention are seven kinds of FITC, Alexa Fluor 488, PE, PE-Cy5, PE-Cy7, PerCP, and APC. At the top of each table, Channel is the detector channel type of the flow cytometer, Tandem-dye is the type of fluorescence attached to the antibody, Marker is the type of marker marker, and Marker location is the location of each marker. As described below, there may be a difference in the experimental method.

NK cells (NKC)

Channel	Tandem-dye	Marker	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	surface	BD	555339	6125658	0.5
FL2	PE	CD56	surface	BD	555516	6054620	2.5
FL3	PE-Cy7	CD314	surface	BD	562365	6140911	2.5
FL4	APC	CD158b	surface	BioLegend	312612	B210467	0.5

Th1Th2 (TH)

Channel	Tandem-dye	Target	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	Alexa Fluor488	CD183	surface	BD	558047	6155849	0.5
FL2	PE	CD194	surface	BD	551120	5107877	0.5
FL3	PE-Cy5	CD4	surface	BD	555348	5037589	0.5
FL4	APC	CD196	surface	BD	560619	5135834	0.15

Myeloid Derived Stem Cells (MDSCs)

Channel	Tandem-dye	Target	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	surface	BD	555339	6125658	0.5
FL1	FITC	CD19	surface	BD	555412	5097663	0.5
FL1	FITC	CD56	surface	BD	340410	6141554	2.5
FL2	PE	CD11b	surface	BD	555388	4314750	0.1
FL3	PE-Cy5	HLA-DR	surface	BD	555813	6132725	2.5
FL4	APC	CD33	surface	BD	551378	4288542	0.1

Regulatory T cells (Tregs)

Channel	Tandem-dye	Target	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD4	surface	BD	555346	5097644	0.5
FL2	PE	CD25	surface	BD	555432	6040885	2.5
FL3	PE-Cy7	CD152	intra	BD	555854	5142830	2.5
FL4	APC	CD279	surface	BD	558694	6154800	2.5

Cytotoxic T cells (CTLs)

Channel	Tandem-dye	Target	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	surface	BD	555339	6125658	0.5
FL2	PE	CD25	surface	BD	555432	6040885	2.5
FL3	PE-Cy7	CD152	intra	BD	555854	5142830	2.5
FL4	APC	CD279	surface	BD	558694	6154800	2.5

Exhausted T cells (ETc)

Channel	Tandem-dye	Target	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	surface	BD	555339	6125658	0.5
FL2	PE	TIGIT	Surface	eBioseience	12-9500-42	4310012	2.5
FL3	PerCP	CD8	Surface	eBioseience	46-0087-41	E10832-1634	2.5
FL4	APC	CD279	Surface	BD	558694	6154800	2.5

Immune checkpoint (ICP)

Channel	Tandem-dye	marker	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	Surface	BD	555339	6125658	0.5
FL2	PE	CD366	Intra	BD	563422	5082811	One
FL3	PerCP	CD272	Intra	R & D systems	FAB3354C	ABCC212071	One
FL4	APC	CD223	intra	R & D systems	FAB23193A	ADXM0116041	2.5

Gamma-delta T cells (GDT)

Channel	Tandem-dye	marker	Marker location	Distributor	Catalog #	Lot #	Volume / test
FL1	FITC	CD3	Surface	BD	555339	6125658	0.5
FL2	PE	γδ TCR	surface	BD	555717	5267944	One

The basic staining method was carried out as follows.

① Dispense the volume per test (volume / test) of each antibody to be stained into a microcentrifuge tube of 1.8 mL volume using a micro pipette. The combined volume of each antibody is prepared to be 10 μL per test. In addition, the preparation of the antibody varies depending on whether the marker location to be stained is all the surface of the membrane or the combination of the surface and the cytoplasm. The basic staining is only the surface marker. The cells are stained by combining antibodies for staining, fixation of cells, and then prepared by staining intracellular markers.

For example, if you want to analyze NK cells (NKC) consisting of a combination of surface membranes for 10 tests (10 people), 5 μL (0.5 μL x 10 tests) of FITC mouse anti-human CD3 IgGs. ), 25 μL (2.5 μL × 10 tests) of PE mouse anti-human CD56 IgGs, 25 μL (2.5 μL × 10 tests) of PE-Cy7 mouse anti-human CD314 IgGs, 5 μL (0.5 μL of APC mouse anti-human CD158b IgG) x 10 tests) were put into each microcentrifuge tube to prepare 60 μL. Then, 40 μL of PBS (Phosphate-buffered saline) was added to the tube to make a final volume of 100 μL of antibody combination.

As another example, if you want to analyze Tregs consisting of a combination of the surface and the cytoplasm of the marker with 10 tests (10 persons), prepare two antibody combination tubes.

First, in the first tube, 5 μL (0.5 μL x 10 tests) of FITC mouse anti-human CD4 IgGs, surface staining antibody, 25 μL (2.5 μL x 10 tests) of PE mouse anti-human CD25 IgGs, APC mouse anti Prepare 5 μL (0.5 μL × 10 tests) of human CD279 IgG into microcentrifuge tubes and prepare 35 μL. Then, 65 μL of PBS (Phosphate-buffered saline) was added to the tube to make a final volume of 100 μL antibody combination.

Then, prepare 25 μL (2.5 μL × 10 tests) of PE-Cy5 mouse anti-human CD152 IgGs, intra-staining antibody, in a second tube and put 75 μL of Phosphate-buffered saline (PBS) into the tube. Make an antibody combination volume.

In this way, the staining antibody of NK cells (NKC), Th1Th2 (TH), Myeloid Derived Stem Cells (MDSCs), Exhausted T cells (ETc), Gamma-delta T cells (GDT) consisting of a combination of cell membranes Prepare staining antibodies of Regulatory T cells (Tregs), Cytotoxic T cells (CTLs), and Immune checkpoint (ICP), each of which consists of a combination of cell membrane and cytoplasm, for cell membrane and cytoplasmic staining. Constituent volume ratio of each antibody is as indicated through the volume / test ratio of Table 1 to Table 8, the antibody combination volume can be prepared by the same method as the above example.

② Once all the antibody combination tubes for staining are prepared, dispense 50 μL of whole blood into each of eight polystyrene tubes.

③ From the tube containing the prepared antibody combination (antibody combination for cell membrane), dispense 10 μL of the antibody combination into the test tube in the blood and mix well with a micropipette. Then, dye for 20 minutes at room temperature with light blocking.

④ After cell staining, dispense 450 μL of lysing solution (BD FACS Lysing solution, DB Biosciences) into each of 8 polystyrene tubes and mix well with vortex to remove red blood cells (RBC). React.

⑤ Dispense 2 mL of PBS into each tube and place in a centrifuge for 250 minutes for 5 minutes. Turn to G force. Then discard the supernatant and wash the cells in the same centrifuge once more with 2 mL of PBS.

⑥ If stained with a combination antibody of the cell membrane (surface) only 200μL PBS into the tube and mix well and proceed with the flow cytometer.

⑦ In the case of the antibody of the combination of the surface membrane and the cytoplasm (intra) staining, ⑤ 40 μL of the distilled water containing 0.05% saponine was added to the tube at the same time with 10 μL of the antibody for intracellular staining and mixed well. Then, secondary staining is performed for 20 minutes while blocking light at room temperature. Then, ⑤ process 200μL PBS into the tube and proceed with the flow cytometer.

The results obtained through flow cytometry are all obtained in the distribution (%), and the number of cells (cells / μL) according to each distribution is multiplied by the distribution using the differential of the white blood cells obtained using the automatic blood cell analyzer. It can be calculated as

For example, if CD3-CD56 + NK cells = 15%, the cell count is calculated as 3000 x 0.15 = 450 cells / μL using lymphocyte cells 3000 cells / μL.

The results of analyzing the markers of immune cells using the flow cytometer through the above experimental procedure are shown in FIGS. 1 to 8, and the statistical results of analyzing the colorectal cancer patients and normal persons are shown in Tables 9 to 18. For reference, FIGS. 1 to 8 and 10 are rotated 90 degrees in the counterclockwise direction, it will be described below that the above drawings are based on the rotated 90 degrees in the clockwise direction.

Figure 1 is for the NKC analysis according to a preferred embodiment of the present invention ① CD3-CD56 +, ② CD3 + CD56 +, ③ CD314 + CD158b- in CD3-CD56 + cells (NK cells), ④ CD314-CD158b + in NK cells, ⑤ It provides six cell phenotypes: CD314-CD158b + in NK cells, ⑥ CD158b + in CD3 + CD56- (T cells).

More specifically, in the upper left graph of FIG. 1, when analyzing blood with a flow cytometer, cells can be classified into X-axis as FSC-H (relative size of cells) and Y-axis as SSC-H (cell wrinkles). Blood immune cells can be classified into lymphocytes, monocytes, and granulocytes according to cell size and extent of wrinkles. The analysis was performed mainly on the lymphocytes (Lymphocytes) in the center of the graph, and this part was stained using an antibody attached to each marker. In the upper right graph, the X-axis shows the presence of CD3 staining and the Y-axis shows the presence of CD56 staining. The cross-shaped solid line inside the graph shows CD3- + on the left and CD56 + on the bottom. In this case, the CD3-CD56 + part was designated as Q1, the CD3 + CD56 + part as Q2, the CD3 + CD56- part as Q3, and the CD3-CD56- part as Q4. In the lower left graph, cells of the Q1 region were again classified according to the presence or absence of the CD314 and CD158 markers using antibodies, and the graph expression method was as described above. In the lower right graph, the cells of the Q3 region of the upper right graph were again classified according to the presence or absence of the CD314 and CD158 markers using antibodies, and the expression method of the graph was as described above.

Figure 2 provides four cell phenotypes, such as ① Th1, ② Th2, ③ Th17, ④ Th1 / Th2 for Th1 / Th2 analysis according to a preferred embodiment of the present invention.

More specifically, Figure 2 also shows the results of the analysis by using the cells of the lymphocyte site of Figure 1 by sorting the presence or absence of the staining of each marker CD4, CD183, CD194, CD196 sequentially or simultaneously, graph The reaction between the X-axis and the Y-axis antibody was expressed as a numerical value, and each region was subdivided by a solid line in the graph. The detailed method is as described with reference to FIG. 1. On the other hand, the distribution of Th1, Th2, Th17 is obtained by the following equation (1).

Figure 3 provides a cell phenotype for analysis of Myeloid Derived Stem Cells (MDSCs) according to a preferred embodiment of the present invention.

In more detail, the method of expressing the graph is as described with reference to FIG. MDSCs can be obtained as shown in Equation 2 below.

Figure 4 provides three cell phenotypes, such as ① CD4 + CD279 +, ② CD4 + CD25 +, ③ CD4 + CD152 + for the analysis of Regulatory T cells (Tregs) according to an embodiment of the present invention. Since the representation of the graph shown through FIGS. 4 to 8 is the same as that described with reference to FIG. 1, a detailed description thereof will be omitted.

Figure 5 provides two cell phenotypes, such as ① CD152 + in CTLs, ② CD279 + in CTLs for the analysis of Cytotoxic T cells (CTLs) according to a preferred embodiment of the present invention.

Figure 6 provides a CD279 + TIGIT + in CTLs cell phenotype for Exhausted T cells (ETc) analysis according to a preferred embodiment of the present invention.

FIG. 7 shows ① CD3 + CD366 +, ② CD3-CD366 +, ③ CD366 + in lymphocytes, ④ CD3 + CD272 +, ⑤ CD3-CD272 +, ⑥ CD272 + in lymphocytes, for Immune checkpoint (ICP) analysis according to a preferred embodiment of the present invention. ⑦ CD3 + CD223 +, ⑧ CD3-CD223 +, ⑨ CD223 + In lymphocytes, it provides nine cell phenotypes.

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

On the other hand, Table 9 is a table showing the average distribution and cell number of peripheral blood NKC of colorectal cancer patients and normal people. Table 10 is a table showing the average distribution and cell number of peripheral blood TH of colorectal cancer patients and normal people. Table 11 is a table showing the average distribution and cell number of peripheral blood MDSCs of colorectal cancer patients and normal people. Table 12 is a table showing the average distribution and cell number of peripheral blood Tregs of colorectal cancer patients and normal people. Table 13 is a table showing the average distribution and cell number of peripheral blood CTLs of colorectal cancer patients and normal people. Table 14 is a table showing the average distribution and cell number of peripheral blood ETc of colorectal cancer patients and normal people. Table 15 is a table showing the average distribution and cell number of peripheral blood ICP of colorectal cancer patients and normal people. Table 16 is a table showing the average distribution and cell number of peripheral blood GDT of colorectal cancer patients and normal people. Table 17 is a table showing the average distribution and cell number of peripheral blood WBCS of colorectal cancer patients and normal people. Table 18 is a table showing the ratio of peripheral blood immune cells of colorectal cancer patients and normal people.

In the upper column of each table, N is the number of experimental groups, Mean is the mean value, SD is the standard deviation, SE is the standard error, and 95% Mean is calculated using only 95% by discarding 2.5% of the results at both ends of the group to reduce the error rate The mean value, Median, means the median value.

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
NKC	CD3-CD56 + (NK)%	Control	132	16.50	8.08	0.70	16.00	14.82	0.183
		Patients	98	17.79	10.05	1.28	17.20	16.21
NKC	CD3 + CD56 +%	Control	132	5.00	4.17	0.36	4.45	3.77	0.065
		Patients	98	3.67	2.50	0.32	3.49	3.00
NKC	CD3-CD56 + cells / μL	Control	132	342	198	17	325	313	0.284
		Patients	98	345	206	26	330	304
NKC	CD3 + CD56 + (NKT) cells / μL	Control	132	108	109	10	93	76	0.129
		Patients	98	76	64	8	70	56
NKC	CD314 + CD158b - %  in NK cells	Control	132	56.19	16.45	1.48	56.60	57.14	0.030
		Patients	98	47.04	18.02	2.29	47.12	49.41
NKC	CD314 + CD158b- cells / μL in NK cells	Control	132	181	151	13	178	155	0.084
		Patients	98	162	114	14	152	133
NKC	CD314- CD158b + %  in NK cells	Control	132	1.82	2.51	0.23	1.47	1.11	0.000
		Patients	98	6.46	5.67	0.72	6.00	5.08
NKC	CD314- CD158b + cells / μL  in NK  cells	Control	132	6	9	One	5	3	0.000
		Patients	98	20	19	2	19	13
NKC	CD314 + %  in CD3 + CD56- (T cells)	Control	132	41.98	9.53	0.86	41.93	41.31	0.007
		Patients	98	33.13	10.40	1.32	32.73	33.11
NKC	CD314 + cells / μL in T cells	Control	132	541	292	25	561	524	0.543
		Patients	98	449	222	28	436	428
NKC	CD158b +% in T cells	Control	132	5.84	10.28	0.93	4.32	3.51	0.578
		Patients	98	5.79	5.19	0.66	5.06	4.12
NKC	CD158b + cells / μL in T cells	Control	132	76	134	12	60	48	0.477
		Patients	98	79	83	11	68	53

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
TH	CD4 + %	control	132	38.02	7.90	0.69	38.11	38.00	0.000
		Patients	98	43.08	10.01	1.27	43.12	42.32
TH	CD4 + cells / μL	control	132	780	249	22	774	771	0.002
		Patients	98	905	413	52	885	865
TH	Th1 %	control	132	14.21	5.17	0.45	14.05	13.51	0.009
		Patients	98	13.93	5.22	0.66	13.68	13.52
TH	Th1 cells / μL	control	132	109	50	4	108	101	0.757
		Patients	98	123	72	9	116	109
TH	Th2 %	control	132	13.36	3.57	0.31	13.18	12.83	0.001
		Patients	98	16.45	4.60	0.58	16.42	15.74
TH	Th2  cells / μL	control	132	102	37	3	100	97	0.000
		Patients	98	143	66	8	141	144
TH	Th17%	control	132	9.50	3.15	0.27	9.43	9.16	0.633
		Patients	98	9.54	3.90	0.49	9.32	8.58
TH	Th17  cells / μL	control	132	72	28	2	71	70	0.003
		Patients	98	85	57	7	79	78

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
MDSCs	MDSCs%	control	132	35.70	13.40	1.17	35.51	34.96	0.274
		Patients	98	44.13	16.19	2.06	44.75	47.21
MDSCs	MDSCs  cells / μL	control	132	2212	1227	107	2111	1938	0.005
		Patients	98	3333	1815	231	3231	3132

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
Tregs	CD4 + CD279 + (PD-1) %	control	132	7.07	2.42	0.21	6.92	7.00	0.000
		Patients	98	12.59	4.84	0.61	12.30	12.07
Tregs	CD4 + CD279 + cells / μL	control	132	146	62	5	142	132	0.000
		Patients	98	256	131	17	248	224
Tregs	CD4 + CD25 + %	control	132	15.54	4.36	0.38	15.38	15.85	0.000
		Patients	98	21.17	5.92	0.75	21.31	21.31
Tregs	CD4 + CD25 + cells / μL	control	132	317	119	10	310	295	0.003
		Patients	98	445	230	29	427	402
Tregs	CD4 + CD152 + ( CTLA -4) %	control	132	5.07	1.72	0.15	4.93	4.76	0.000
		Patients	98	8.51	4.75	0.60	8.01	7.32
Tregs	CD4 + CD152 + cells / μL	control	132	103	40	3	100	99	0.000
		Patients	98	169	92	12	163	145

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
CTLs	CD3 +%	control	132	65.37	8.88	0.77	65.71	65.82	0.116
		Patients	98	65.37	10.45	1.33	65.45	67.05
CTLs	CD3 + cells / μL	control	132	1353	426	37	1339	1309	0.149
		Patients	98	1371	553	70	1355	1379
CTLs	CD3 + CD8 +%	control	132	27.37	7.96	0.69	26.92	26.28	0.014
		Patients	98	22.30	7.09	0.90	22.10	21.41
CTLs	CD3 + CD8 cells / μL	control	132	574	268	23	550	514	0.282
		Patients	98	466	228	29	451	426
CTLs	CD279 +% in CTLs	control	132	21.55	12.63	1.10	20.13	19.31	0.000
		Patients	98	33.82	10.79	1.37	33.54	32.25
CTLs	CD279 + cells / μL in CTLs	control	132	122	88	8	111	90	0.069
		Patients	98	147	68	9	141	140
CTLs	CD152 +% in CTLs	control	132	19.13	12.17	1.06	17.29	16.79	0.000
		Patients	98	24.30	9.92	1.26	23.62	22.16
CTLs	CD152 + cells / μL in CTLs	control	132	104	67	6	95	89	0.010
		Patients	98	108	59	8	103	105

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
ETc	CD279 + TIGIT +% in CTLs	control	132	15.09	8.99	0.82	14.32	12.54	0.000
		Patients	98	26.50	9.68	1.71	26.99	26.02

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
ICP	CD3-CD366 + (TIM3)%	Control	132	14.81	7.26	0.63	14.44	13.74	0.609
		Patients	98	17.00	8.84	1.14	16.18	15.94
ICP	CD3-CD366 + cells / μL	Control	132	307	180	16	293	277	0.358
		Patients	98	315	183	23	308	301
ICP	CD3 + CD366 +%	Control	132	5.78	2.67	0.23	5.62	5.37	0.295
		Patients	98	6.20	3.14	0.41	5.98	5.43
ICP	CD3 + CD366 + cells / μL	Control	132	119	65	6	114	100	0.436
		Patients	98	123	84	11	120	108
ICP	CD366 +% in Lymphocytes	Control	132	20.61	7.02	0.61	20.29	19.64	0.892
		Patients	98	23.20	8.47	1.09	22.46	22.85
ICP	CD366 + cells / μL in Lymphocytes	Control	132	427	195	17	413	384	0.574
		Patients	98	438	203	26	436	422
ICP	CD3-CD272 + (BTLA)%	Control	132	4.94	2.40	0.21	4.70	4.53	0.889
		Patients	98	5.33	2.91	0.38	4.98	4.51
ICP	CD3-CD272 + cells / μL	Control	132	99	54	5	94	90	0.427
		Patients	98	96	48	6	94	90
ICP	CD3 + CD272 +%	Control	132	5.81	2.58	0.22	5.64	5.28	0.019
		Patients	98	7.04	3.37	0.43	6.71	6.04
ICP	CD3 + CD272 + cells / μL	Control	132	120	69	6	114	111	0.099
		Patients	98	137	79	10	135	122
ICP	CD272 +% in Lymphocytes	Control	132	10.73	3.82	0.33	10.52	10.42	0.134
		Patients	98	12.37	5.15	0.66	11.94	10.63
ICP	CD272 + cells / μL in Lymphocytes	Control	132	219	106	9	210	207	0.460
		Patients	98	233	111	14	235	232
ICP	CD3-CD223 + (LAG3)%	Control	132	4.67	3.67	0.32	4.17	3.94	0.344
		Patients	98	6.42	4.50	0.59	5.95	5.15
ICP	CD3-CD223 + cells / μL	Control	132	93	64	6	86	73	0.374
		Patients	98	117	89	11	114	108
ICP	CD3 + CD223 + (LAG3)%	Control	132	4.29	2.46	0.21	4.07	3.93	0.000
		Patients	98	6.91	4.32	0.56	6.54	6.01
ICP	CD3 + CD223 + cells / μL	Control	132	90	65	6	83	73	0.001
		Patients	98	135	101	13	133	129
ICP	CD223 +% in Lymphocytes	Control	132	9.00	4.70	0.41	8.63	8.00	0.003
		Patients	98	13.34	7.42	0.97	12.75	11.60
ICP	CD223 + cells / μL in Lymphocytes	Control	132	183	105	9	175	161	0.013
		Patients	98	252	166	21	252	232

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
GDT	TCR γδ%	control	132	6.86	5.74	0.50	6.13	5.10	0.825
		Patients	98	4.58	3.98	0.51	4.10	3.03
GDT	TCR γδ cells / μL	control	132	142	128	11	125	108	0.593
		Patients	98	95	94	12	83	70

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
WBCs	WBC cells / μL	control	132	5973	1565	136	5887	5750	0.000
		Patients	98	7369	2221	282	7206	7100
WBCs	Lymphocytes%	control	132	35.43	8.51	0.74	35.40	35.90	0.005
		Patients	98	29.11	9.67	1.23	28.99	30.35
WBCs	Neutrophils%	control	132	54.41	9.91	0.86	54.52	53.95	0.010
		Patients	98	61.24	10.78	1.37	61.04	59.65
WBCs	Neutrophils cells / μL	control	132	3311	1275	111	3217	3099	0.000
		Patients	98	4623	2081	264	4401	4145
WBCs	Neutrophils / Lymphocytes	control	132	1.72	0.88	0.08	1.63	1.52	0.007
		Patients	98	2.58	1.66	0.21	2.38	1.93
WBCs	Lymphocytes cells / μL	control	132	2066	574	50	2050	2009	0.485
		Patients	98	2056	702	89	2032	2085
WBCs	RBC	control	132	4.63	0.41	0.04	4.63	4.67	0.066
		Patients	98	4.28	0.59	0.08	4.27	4.24
WBCs	Hemoglobin	control	132	13.83	1.31	0.13	13.83	13.60	0.071
		Patients	98	12.83	2.49	0.32	12.77	12.80
WBCs	monocytes%	control	132	6.81	1.86	0.18	6.77	6.60	0.042
		Patients	98	6.11	2.62	0.33	5.96	5.90
WBCs	eosinophil%	control	132	2.63	2.21	0.21	2.40	2.10	0.004
		Patients	98	1.26	1.34	0.17	1.05	0.90
WBCs	basophil%	control	132	0.46	0.31	0.03	0.43	0.40	0.000
		Patients	98	0.28	0.18	0.02	0.26	0.20

Group statistics
Category	Parameter	State	N	Mean	S.D.	S.E.	95% Mean	Median	P value
Ratio	CD4 / CD8 ratio	control	132	1.53	0.63	0.05	1.49	1.42	0.001
		Patients	98	2.20	1.09	0.14	2.10	2.06
Ratio	CD3 + CD8 + / CD3-CD56 + (CTNR)	control	132	2.27	1.96	0.17	2.06	2.00	0.253
		Patients	98	1.92	1.63	0.21	1.74	1.39
Ratio	CTLs / Treg	control	132	1.97	1.06	0.09	1.87	1.71	0.019
		Patients	98	1.21	0.75	0.10	1.12	1.05
Ratio	Th17 / Treg	control	132	0.69	0.40	0.04	0.68	0.69	0.220
		Patients	98	0.50	0.28	0.04	0.47	0.44
Ratio	TH1 / TH2 ratio	control	132	1.15	0.55	0.05	1.13	1.00	0.000
		Patients	98	0.91	0.43	0.05	0.89	0.92

Binary Logistic  Rectal Colorectal Cancer Diagnosis Using Regression Model

As shown in Tables 9 to 18, peripheral blood immune cells of 132 normal and 98 patients were analyzed. As a result, markers of immune cells showing a difference between the two groups at the significance level P value <0.05 were found. The bold and italicized parts of the table are significant markers of immune cells. The difference in mean value between the two groups was analyzed using the statistical program SPSS. As the population follows the normal distribution and satisfies the equivariance condition, the statistical difference of the mean value was used by the T test student's t - test.

Through this, the distribution and number of cells of each immune cell in peripheral blood can be measured and cancer colony-specific immune cell markers can be used to accurately classify colorectal cancer patients and normal persons, and cancer diagnosis through immunological tests may be possible. In order to reliably diagnose the cancer, a binary logistic regression model formulated to express the difference in peripheral blood immunity between colorectal cancer patients and normal people was applied to the diagnosis of cancer.

To complete the algorithm, colorectal cancer patients and normal subjects were converted to 1 and 0, respectively, as dependent variables, and immune cell marker result values were used as independent variables as shown in Tables 9 to 18 above. In addition, the items that best distinguish the two groups of 132 normal and 98 colorectal cancer patients were selected.

Variables in the Equation
		B	S.E.	Wald	df	Sig.	Exp (B)	95% C.I.for Exp (B)
								Lower	Upper
Step 1a	CD4 +%	-.403	.183	4.841	One	.028	.668	.467	.957
Step 1a	CD3 + CD8 +%	-.468	.175	7.183	One	.007	.626	.445	.882
Step 1a	CD4 + CD279 +%	.961	.380	6.400	One	.011	2.613	1.242	5.500
Step 1a	CD4 + CD25 +%	.646	.248	6.803	One	.009	1.908	1.174	3.102
Step 1a	CD4 + CD152 +%	.001	.359	.000	One	.997	1.001	.496	2.023
Step 1a	CD279 +% in CTLs	-.093	.061	2.328	One	.127	.911	.809	1.027
Step 1a	CD152 +% in CTLs	.131	.063	4.371	One	.037	1.140	1.008	1.290
Step 1a	CD3 + CD272 +%	.623	.246	6.425	One	.011	1.865	1.152	3.020
Step 1a	CD3 + CD223 +	.479	.265	3.265	One	.071	1.614	.960	2.714
Step 1a	Lymphocytes%	-.221	.152	2.114	One	.146	.801	.595	1.080
Step 1a	Neutrophils%	-.174	.103	2.863	One	.091	.840	.687	1.028
Step 1a	NLR	-1.056	1.043	1.025	One	.311	.348	.045	2.687
Step 1a	CTLs / Treg	4.576	1.669	7.518	One	.006	97.096	3.687	2556.975
Step 1a	CD314 + CD158b-% in NK cells	-.011	.025	.203	One	.652	.989	.942	1.038
Step 1a	CD314-CD158b +% in NK cells	.739	.254	8.427	One	.004	2.093	1.271	3.447
Step 1a	CD314 + in T cells	-.140	.113	1.541	One	.215	.870	.698	1.084
Step 1a	Th1%	.450	.272	2.734	One	.098	1.568	.920	2.673
Step 1a	Th2%	.074	.196	.141	One	.707	1.076	.733	1.582
Step 1a	TH1 / TH2	-7.516	3.871	3.769	One	.052	.001	.000	1.075
Step 1a	MDSCs cells / μL	-.001	.000	2.133	One	.144	.999	.998	1.000
Step 1a	monocytes%	.495	.284	3.033	One	.082	1.640	.940	2.861
Step 1a	eosinophil%	-1.866	.681	7.502	One	.006	.155	.041	.588
Step 1a	basophil%	-13.906	4.758	8.543	One	.003	.000	.000	.010
Step 1a	Constant	23.317	16.399	2.022	One	.155

Table 19 above shows coefficients (B) and constants in a regression analysis model using 23 immune cell markers. In the items at the top of the table, B is a B estimate and corresponds to a coefficient value in a regression model. SE is the standard error value for the B estimate, and Wald is the square of (the standard error value of the B estimate / B estimate). In other words, it means (B / SE) ^2, which means a statistic for significance test of each independent variable. Df means degrees of freedom, Sig. Means significance, and the significance of each item in the model, Exp (B) means e ^B with natural logarithm of ^B , and each independent variable increases by 1. This is a statistic that indicates how many times the probability of belonging to a group having an internal value of 1 is greater than that of a group having an internal value of 0.

The logistic regression formula using the constant and coefficient (B) is as follows.

= 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 (CD314 + CD158b-% in NK cells) +0.739 (CD314-CD158b +% in NK cells) -0.140 (CD314 + in T cells) +0.450 (Th1%) + 0.074 (Th2%)-7.516 (TH1 / TH2) -0.001 (MDSCs cells /μL)+0.495 (monocytes%)-1.866 (eosinophil%)-13.906 (basophil%)

However, in the case of algorithms using 23 items, sensitivity and specificity may tend to be inferior when a blind test is performed on cancer patients and normal people. And since there are so many 23 items that make up the logistic regression function, in fact, among the 23 items, the result value can be largely influenced by the higher weight item, and the data for more factors are required. May act as an inhibitor in testing. Therefore, more preferably, the following logistic regression function consisting of a combination of 11 factors can be used.

= -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)

As a result of flow cytometry analysis of immune cells to obtain the result value, each result value and the coefficient value are multiplied and summed together with a constant value to obtain a Logit (P) value. In this case, as the purpose of diagnosing cancer by using a regression formula is to replace the colorectal cancer patients and normal people with 1 and 0, it is necessary to input any new test result value into the algorithm to see what value is observed between 1 and 0. It is necessary to identify and predict whether or not people with colorectal cancer are normal. Therefore, Logit (P), a linear equation model value, can be converted to converge to the values of 1 and 0 using the exponential function. In this case, a predictive formula classified into colorectal cancer patients (representing a value of 1) and normal persons (representing a value of 0) by using an exponential function is shown in Equation 3 below.

As shown in Equation 3, the predictive Y value of colorectal cancer patients and normal subjects is e ^P Is obtained by denominator 1-e ^P as the denominator, and the probability Y value is named E score in the present invention.

9 is a receiver operating characteristic curve made using the E score according to an embodiment of the present invention.

As shown in FIG. 9, when the receiver operating characteristic (ROC) curve for the E score is drawn, it can be seen that the AUC value is 0.98, which means that colorectal cancer can be diagnosed based on peripheral blood immunity. have.

cut value	sensitivity ( % )	specificity ( % )	Youden's  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 sensitivity, specificity, and Youden index values according to the E score result value. As shown in Table 20, cut values for sensitivity and specificity of the E score result value can be arbitrarily adjusted. When the cut value is 0.098, the sensitivity is 100% and the specificity is 78.4%. When the cut value is 0.684, the sensitivity is 83.1% and the specificity is 100%. This indicates that all of them are normal at E score <0.098 and similarly diagnosed as colorectal cancer patients at 0.684 <E score.

In other words, we design an exponential function where the values of cancer patients and normal people converge to 1 and 0 using the values of the linear equations, and then obtain the optimal cut value between 0 and 1 through the Youden index on the ROC curve. As a result, a binary logistic regression algorithm can be designed to diagnose cancer.

In addition, randomly blind tests of new normal and cancer patients using the values calculated through the retrospective designed algorithm can be used to prospectively evaluate the immunity of peripheral blood and diagnose cancer. do.

10 is a diagram showing a model for providing cancer immunity in three steps of E1 E2 E3 using two E score cut values according to an embodiment of the present invention.

Referring to FIG. 10, it can be seen that it is not necessary to define cut values as one in a logistic regression analysis using peripheral blood immunity. Therefore, according to the present invention, the cut value of the E score may be divided into three sections based on 0.098 and 0.684, and the cancer immunity according to the E score result may be classified into three stages. Section E socre ≤ 0.098 is normal and named E1. Section 0.098 <E score ≤ 0.684 is a high-risk cancer group and is named E2. 0.684 <E score is colorectal cancer patients.

In the present invention, a patient before surgery for colorectal cancer is diagnosed as a normal person (E1), a high risk group of cancer (E2), and a colorectal cancer patient (E3) using, for example, peripheral blood immunity. Of course, it can also be applied to species. In addition, the regression model made using 23 or 11 immune cell markers can maximize the usefulness of cancer diagnosis by increasing the sensitivity and specificity by using newly discovered markers, and the 23 or 11 proposed in the present invention. New combinations, rather than branch items, can also be used to diagnose and regress models.

Although the present invention has been described by specific embodiments such as specific components and the like, but the embodiments and the drawings are provided to assist in a more general understanding of the present invention, the present invention is not limited to the above embodiments. However, one of ordinary skill in the art will be able to make various modifications and variations from this description.

Accordingly, the spirit of the present invention should not be limited to the above-described embodiments, and all of the equivalents or equivalents of the claims, as well as the appended claims, belong to the scope of the present invention. something to do.

Claims (23)

1. In the method of evaluating the immunity of peripheral blood using the difference in the distribution of immune cells in peripheral blood of colorectal cancer patients and normal people, and using the same to provide information on the presence of colorectal cancer,

   (A) classifying lymphocytes, monocytes, and granulocytes by analyzing the medium cell size and the degree of wrinkles of peripheral blood immune cells;

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

   (C) determining a combination of differences in cancer markers and normal persons with statistically significant result values of the marker markers in the two cancer patients and normal populations so as to determine whether cancer has occurred; And

   (D) a method for diagnosing the presence of colorectal cancer by measuring the immunity per blood without counting natural killer cells using a flow cytometer or a cell counter using the marker marker.
2. The method of claim 1,

   Immune cells in the step (A)

   1) NK cells (NKC)

   2) Th1Th2 (TH)

   3) Myeloid Derived Stem Cells (MDSCs)

   4) Regulatory T cells (Tregs)

   5) Cytotoxic T cells (CTLs)

   6) Exhausted T cells (ETc)

   7) Immune checkpoint (ICP)

   8) Gamma-delta T cells (GDT)

   9) White blood cell subtype (WBCS)

   The method characterized in that the analysis divided into nine groups.
3. The method of claim 2,

   In the step (B), NK cells (NKC), Th1Th2 (TH), Myeloid Derived Stem Cells (MDSCs), Regulatory T cells (Tregs), Cytotoxic T cells (CTLs), Exhausted T cells (ETc), Immune checkpoint ( ICP), or Gamma-delta T cells (GDT) cell analysis is performed using a flow cytometer, and WBC, Lymphocytes, Neutrophils, Monocytes, Basophils, or Eosinophils cell assays included in the white blood cell subtype (WBCS) Method through the analyzer.
4. The method of claim 2,

   In the step (B),

   NK cells (NKC), Th1Th2 (TH), Myeloid Derived Stem Cells (MDSCs), Exhausted T cells (ETc), Gamma-delta T cells (GDT) immune cells were fluorescently stained cell surface marker markers,

   Regulatory T cells (Tregs), Cytotoxic T cells (CTLs), and Immune checkpoint (ICP) immune cells are characterized by fluorescence staining of cell surface or intracellular marker markers for analysis using a flow cytometer. How to.
5. The method of claim 1,

   Method (B) is characterized in that the analysis of the phenotype of the immune cells for each marker based on the markers expressed in the immune cells in the distribution (%), the number of cells and the ratio thereof.
6. The method of claim 1,

   In step (C), a method is characterized by deriving a P value by a statistical method using a combination of meaningful marker markers that can classify cancer patients and normal persons, and determining whether the P value satisfies a predetermined criterion. .
7. The method of claim 6,

   NK cells (NKC) based on average distribution and number of cells CD314-CD158b-% in NK cells, CD314-CD158b +% in NK cells, CD314-CD158b + cells / μL in NK cells, CD314 + in CD3 + CD56- (T cells ) Wherein the P value satisfies a predetermined criterion.
8. The method of claim 6,

   Th1Th2 (TH) is characterized in that the P value meets a predetermined criterion in CD4 +%, CD4 + cells / μL, Th1%, Th2%, Th2 cells / μL, Th17 cells / μL based on the average distribution and the number of cells. Way.
9. The method of claim 6,

   Myeloid Derived Stem Cells (MDSCs) in the MDSCs cells / μL based on the average distribution and the number of cells characterized in that the P value meets a predetermined criterion.
10. The method of claim 6,

    Regulatory T cells (Tregs)% CD4 + CD279 + (PD-1)%, CD4 + CD279 + cells / μL, CD4 + CD25 +%, CD4 + CD25 + cells / μL, CD4 + CD152 + (CTLA-) 4)%, wherein the P value at CD4 + CD152 + cells / μL meets a predetermined criterion.
11. The method of claim 6,

    Cytotoxic T cells (CTLs) in the CD3 + CD8 +%, CD279 +% in CTLs, CD152 +% in CTLs, CD152 + cells / μL in CTLs based on the average distribution and the number of cells characterized in that the P value meets the predetermined criteria Way.
12. The method of claim 6,

    Exhausted T cells (ETc) in the CD279 + TIGIT +% in CTLs based on the average distribution and cell number, characterized in that the P value meets a predetermined criterion.
13. The method of claim 6,

    Immune checkpoint (ICP) was determined by CD3 + CD272 +%, CD3 + CD223 + (LAG3)%, CD3 + CD223 + cells / μL, CD223 +% in Lymphocytes, CD223 + cells / μL in Lymphocytes based on average distribution and cell number. Characterized by satisfying this predetermined criterion.
14. The method of claim 6,

    White blood cell subtype (WBCS) was determined based on the mean distribution and the number of cells in the WBC cells / μL, Lymphocytes%, Neutrophils%, Neutrophils cells / μL, Neutrophils / Lymphocytes, monocytes%, eosinophil%, basophil% Characterized by meeting the criteria.
15. The method of claim 6,

    And the P value satisfies a predetermined criterion in the CD4 / CD8 ratio and the TH1 / TH2 ratio based on the ratio of peripheral blood immune cells of cancer patients and normal persons.
16. The method of claim 6,

    The P value is 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 (CD314 + CD158b-% in NK cells) +0.739 (CD314-CD158b +% in NK cells) -0.140 (CD314 + in T cells) +0.450 (Th1%) + 0.074 (Th2%)-7.516 (TH1 / TH2) -0.001 (MDSCs cells / μL) +0.495 (monocytes%)-1.866 (eosinophil%)-13.906 (basophil%).
17. The method of claim 6,

    The P value is -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) How to feature.
18. The method of claim 1,

    In the step (D),

    (D-1) designing a linear equation whose coefficient is a combination of at least one significant marker marker capable of classifying a cancer patient with a normal person;

    (D-2) Designing an exponential function where the values of cancer patients and normal people converge to 1 and 0, respectively, using the values of the linear equation, and obtain the optimal cut value between 0 and 1 through the Youden index on the ROC curve. Then designing a binary logistic regression algorithm for diagnosing cancer on the basis of this; And

    (D-3) A random blind test of new normal and cancer patients using the values calculated through the retrospective designed algorithm to prospectively evaluate the immunity of peripheral blood and diagnose cancer The method comprising the step of.
19. The method of claim 18,

    Combination values of the significant marker markers are CD4 +%, CD3 + CD8 +%, CD4 + CD279 +%, CD4 + CD25 +%, CD4 + CD152 +%, CD279 +% in CTLs, CD152 +% in CTLs, CD3 + CD272 +%, CD3 + CD223 +, Lymphocytes%, Neutrophils%, NLR, CTLs / Treg, CD314 + CD158b-% in NK cells. CD314-CD158b +% in NK cells, CD4314 + in T cells, Th1%, Th2%, TH1 / TH2, MDSCs cells / μL, monocytes%, eosinophil%, to basophil%.
20. The method of claim 18,

    Combination values of these significant marker markers are among CD3, NK, CD4CD279, CD4 + CD25 +, CD4 + CD152, CD3 + CD366 +, CD3 + CD272, CD3 + CD223 +, CD158b + CD314-CD3-CD56 +, Th2, and MDSCs cells / μL And at least one.
21. The method of claim 18,

    In the step (D-3),

    Based on the sensitivity and the specificity, the value calculated through the binary logistic regression algorithm at the point where the sensitivity is a predetermined set value is a first cut value, and the value calculated through the algorithm at the point where the specificity is a predetermined set value. A second cut value is set so that a value calculated by the algorithm is equal to or less than the first cut value value is normal, and a group between the first cut value and the second cut value value is a high risk group of cancer occurrence, and the second cut value. A method for diagnosing a cancer patient with a group above the value.
22. A computer-readable recording medium having recorded thereon a computer program for executing the method according to any one of claims 1 to 21.
23. A diagnostic kit for providing information on the presence of colorectal cancer by the method according to any one of claims 1 to 21.

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