METHOD FOR PREDICTING PROGNOSIS OF PATIENTS HAVING EARLY BREAST CANCER

Abstract

The present invention relates to a method for predicting the prognosis of abreast cancer patient and, more particularly, to a method for predicting the prognosis of breast cancer by combining immune-related genes. The present invention is applicable to all breast cancer patients regardless of the breast cancer molecular subtype, and in addition, if the immune-related gene combination is used to predict the prognosis of breast cancer, as provided in the present invention, it is possible to predict the prognosis of a breast cancer patient without information on proliferation genes.

Claims

1. A method for predicting the prognosis of breast cancer comprising following steps to provide information necessary for predicting the prognosis of a patient's breast cancer: (a) measuring the expression levels of immune-related genes from a biological sample obtained from a patient with breast cancer; (b) standardizing the expression levels measured in step (a); and (c) predicting the prognosis of breast cancer by combining the expression levels of the immune-related genes standardized in step (b), wherein the combined overexpression levels of the immune-related genes are predicted to indicate good prognosis of breast cancer.

2. The method of claim 1, wherein the prognosis of breast cancer is at least one selected from the group consisting of recurrence, metastasis and metastatic recurrence.

3. The method of claim 1, wherein the breast cancer is a subtype selected from the group consisting of HR+/HER2−, HR+/HER2+, HR−/HER2+ and TNBC.

4. The method of claim 1, wherein the breast cancer is early breast cancer classified as LN status 0 (when no metastasis to lymph nodes has occurred) or 1 (when metastasis to lymph nodes has occurred) according to the Tumor Node Metastasis (TNM) system.

5. The method of claim 1, wherein the immune response-related genes are at least two selected from the group consisting of TRBV20-1, CCL19, CD52, SRGN, CD3D, IGJ, HLA-DRA, LOC91316, IGF1, CYBRD1, TMC5, ALDH1A1, OGN, PDCD4, FRZB, CX3CR1, IGFBP6, GLA, LOC96610, IGLL3, ITPR1, SERPINA1, EPHX2, MFAP4, RNASET2, CCNG1, FBLN5, SORBS2, CCBL2, BTN3A2, TFAP2B, LTF, ITM2A, HLA-DPB1, HLA-DMA, RPL3, LOC100130100, FAM129A, ELOVL5, GBP2, RARRES3, GOLM1, RTN1, ICAM3, LAMA2, CXCL13, ZCCHC24, CD37, VTCN1, PYCARD, CORO1A, SH3BGRL, TPSAB1, TNFSF10, ACSF2, TGFBR2, DUSP4, ARHGDIB, TMPRSS3, DCN, LRIG1, FMOD, ZNF423, SQRDL, TPST2, CD44, MREG, GIMAP6, GJA1, IFITM3, BTG2, PIP, RPS9, HLA-DPA1, IMPDH2, TNFRSF17, C14orf139, SPRY2, XBP1, THYN1, APOD, C10orf116, VAV3, FAS, MYBPC1, CFB, TRIM22, ARID5B, PTGDS, TGFBR3, TNFAIP8, SEMA3C, TMEM135, ARHGEF3, PTGER4, ABCA8, ICAM2, HLA-DQB1, HSPA2, CD27, ARMCX1, POU2AF1, IGBP1, PDE4B, ADH1B, WLS, SUCLG2, PGR, STARD13, SORL1, ATP1B1, IFT46, SIK3, LIPT1, OMD, HBB, C3, FGL2, PECI, RAC2, PDZRN3, CXCL12, DPYD, TXNDC15, STOM, EMCN, SCGB2A2, FAM176B, HIGD1A, ACSL5, RPS24, RGS10, RAI2, CNN3, FBXW4, SEPP1, SLC44A4, MGP, ABCD3, SETBP1, APOBEC3G, LCP2, HLA-DRB1, SCUBE2, DEPDC6, RPL15, SH3BP4, MSX2, CLU, DPT, ZNF238, HBP1, GSTK1, ZBTB16, CCDC69, ALDH2, SLC1A1, ARMCX2, HMGCS2, TSPAN3, FTO, PON2, C16orf62, QDPR, LRP2, PSMB8, HCLS1, FXYD1, OAT, SLC38A1, MAOA, LPL, C10orf57, SPARCL1, ERAP2, PDGFRL, RBP4, LRRC17, LHFP, BLNK, HBA2, CST7, TRAT1, IL21R, IGHM, CTLA4, IL2RB, TNFRSF9, CTSW, CCR10, GPR18, CR2, DOCK10, GZMB, ITK, LTB, IGLJ3, IGLV1-44, AIM2, CXCL9, KIAA0125, IL2RG, CD69, CD55, TRAF3IP3, EVI2B, STAP1, KLRB1, PRKCB, GPR171, PPP1R16B, SH2D1A, TNFRSF1B, CD48, BANK1, LY9, VNN2, TCL1A, CYTIP, PTPRC, PDCD1LG2, LTA, IGHG1 and CD19.

6. The method of claim 1, wherein measuring the expression levels of the genes is meant to measure the expression levels of mRNA of the genes or the expression levels of the proteins encoded by the genes.

7. The method of claim 6, wherein measuring the expression levels of mRNA of the genes is meant to measure the expression levels by a pair of primers or probes specifically binding to the genes.

8. The method of claim 6, wherein measuring the expression levels of the proteins is meant to measure the expression levels of antibodies that specifically bind to the proteins.

9. The method of claim 1, wherein the sample is selected from the group consisting of a formalin-fixed paraffin-embedded (FFPE) sample of a tissue containing the patient's cancer cells, a fresh tissue, and a frozen tissue.

10. The method of claim 1, wherein step (c) further comprises a lymph node status in which LN status of 1 (when metastasis to the lymph node has occurred) is predicted to indicate poor prognosis of breast cancer.

11. The method of claim 1, wherein step (c) is to mathematically combine the expression values of the immune-related genes standardized in step (b) to calculate a total score, and the total score indicates the prognosis of patients' breast cancer.

12. The method of claim 11, wherein, when the number of the immune-related genes is n, the mathematical combination is performed by the following Formula 1:
Total score=(β.sub.1*χ.sub.1)+(β.sub.2*χ.sub.2)+ . . . +(β.sub.n*χ.sub.n)  [Formula 1] In the above formula, Xn is the expression value of the n.sup.th gene, and β.sub.n is the Cox Regression estimate of the n.sup.th gene.

13. The method of claim 11, wherein, when the number of the immune-related genes is n, the mathematical combination is performed by the following Formula 2:
Total score={(β.sub.1*χ.sub.1)+(β.sub.2*χ.sub.2)+ . . . +(β.sub.n*χ.sub.n)}+F*LN  [Formula 2] In the above formula, χ.sub.n is the expression value of the n.sup.th gene, β.sub.n is the Cox Regression estimate of the n.sup.th gene, LN is an integer indicating the presence of LN, and F is the Cox Regression estimate for LN.

14. The method of claim 1, wherein the immune-related genes is composed of T Cell Receptor Associated Transmembrane Adaptor 1 (TRAT1), Interleukin 21 Receptor (IL21R), Immunoglobulin Heavy Constant Mu (IGHM), Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4) and Interleukin 2 Receptor Subunit Beta (IL2RB).

15. The method of claim 1, wherein the immune-related genes is composed of TRAT1, IL21R and CTLA4.

16. A method for calculating a breast cancer prognostic risk score, comprising following steps in order to provide information necessary for predicting the prognosis of a patient's breast cancer: (i) measuring the mRNA expression levels of TRAT1, IL21R, IGHM, CTLA4 and IL2RB genes from a biological sample obtained from a patient with breast cancer and the value of LN of the patient with breast cancer; (ii) standardizing the mRNA expression levels of the genes; and (iii) calculating a breast cancer prognostic risk score by substituting the standardized value of step (ii) and the value of LN of step (i) into the following formula 2-1:
risk score={(β.sub.TRAT1*χ.sub.TRAT1)+(β.sub.IL21R*χ.sub.IL21R)+(β.sub.IGHM*χ.sub.IGHM)+(β.sub.CTLA4*χ.sub.CTLA4)+(β.sub.IL2RB*χ.sub.IL2RB)}+F*2*LN.  <Formula 2-1> (In formula 2-1, x is the standardized value of the expression levels of the genes indicated by a subscript, β.sub.TRAT1 is −0.567144 to −0.1952896, β.sub.IL21R is −0.9759746 to −0.3412672, β.sub.IGHM is −0.5428339 to −0.1855019, β.sub.CTLA4 is −0.7454524 to −0.2010003, and β.sub.IL2RB is −1.1701.266 to −1.14698, N is an integer indicating the presence of LN, and F is from 0.3910642 to 1.013551).

17. A method for calculating a breast cancer prognostic risk score, comprising following steps in order to provide information necessary for predicting the prognosis of a patient's breast cancer: (i) measuring the mRNA expression levels of TRAT1, IL21R and CTLA4 genes from a biological sample obtained from a patient with breast cancer and the value of LN of the patient with breast cancer; (ii) standardizing the mRNA expression levels of the genes; and (iii) calculating a breast cancer prognostic risk score by substituting the standardized value of step (ii) and the value of LN of step (i) into the following formula 2-2:
risk score={(β.sub.TRAT1*χ.sub.TRAT1)+(β.sub.IL21R*χ.sub.IL21R)+(β.sub.CTLA4*χ.sub.CTLA4)+F*2*LN.  <Formula 2-2> (In formula 2-2, χ is the standardized value of the expression levels of the genes indicated by a subscript, β.sub.TRAT1 is −1.06659 to −0.2163024, β.sub.IL21R is −0.5429339 to −0.01642154, and β.sub.CTLA4 is −0.5934638 to −0.1644545, N is an integer indicating the presence of LN, and F is from 0.311146 to 0.9303696).

18. The method of claim 16, wherein the method for measuring the expression levels of mRNA of the genes is one selected from the group consisting of microarrays, polymerase chain reaction (PCR), RT-PCR, quantitative RT-PCR (qRT-PCR), real-time polymerase chain reaction (real-time PCR), northern blot, DNA chips and RNA chips.

19. The method of claim 17, wherein the method for measuring the expression levels of mRNA of the genes is one selected from the group consisting of microarrays, polymerase chain reaction (PCR), RT-PCR, quantitative RT-PCR (qRT-PCR), real-time polymerase chain reaction (real-time PCR), northern blot, DNA chips and RNA chips.

20. A composition for predicting the prognosis of patients' breast cancer, comprising a preparation measuring the expression levels of (i) TRAT1, IL21R, IGHM, CTLA4 and IL2RB genes; or (ii) TRAT1, IL21R and CTLA4 genes.

21. The composition of claim 20, wherein the preparation is a preparation for measuring the expression levels of mRNA of the genes; or a preparation for measuring the expression levels of the proteins encoded by the genes.

22. A kit for predicting the prognosis of patients' breast cancer, comprising the composition of claim 20.

23. Use of a preparation for measuring the expression levels of (i) TRAT1, IL21R, IGHM, CTLA4 and IL2RB genes; or (ii) TRAT1, IL21R and CTLA4 genes to prepare an agent for predicting the prognosis of patients' breast cancer.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0153] FIG. 1 shows the overall workflow for the development of new technology for predicting the prognosis of breast cancer using immune genes in the present invention.

[0154] FIG. 2 shows a summary of information on cohorts used in the discovery set and validation set, showing the GEO number, total number of patient samples in the data set, survival types (e.g., DFS, DMFS and OS), treatment types, etc.

[0155] FIG. 3 shows the results of classifying patients into the molecular subtypes of breast cancer according to clinical risk evaluation criteria (based on Adjuvant! Online) and proliferation gene-risk stratification criteria. As shown in FIG. 3, patients were first divided into four groups according to clinical risk assessment criteria (based on Adjuvant! Online) and proliferation gene-risk stratification criteria, and then groups with similar survival rates to the regression analysis results were combined again. Accordingly, one to three subgroups (i.e., AH, AI and AL) were generated for each subtype.

[0156] FIG. 4 shows a Kaplan-Meier plot for DFS/DMFS of each subgroup of AH, AI and AL in the HR+/HER2− subtype.

[0157] FIG. 5 shows a Kaplan-Meier plot for DFS/DMFS of each subgroup of AH and AL in the HR+/HER2+ subtype.

[0158] FIG. 6 shows a Kaplan-Meier plot for DFS/DMFS of the HR−/HER+ subtype group, and all of the HR−/HER+ subtype groups were classified into the AH group.

[0159] FIG. 7 shows a Kaplan-Meier plot for DFS/DMFS of each subgroup of AH and AL in the TNBC (HR−/HER−) subtype.

[0160] FIG. 8 shows two optimal cut-off points (i.e., cutoff-1 and cutoff-2) as criteria for predicting prognosis in the prognostic risk score model using immune genes of the present invention.

[0161] FIG. 9 shows the Kaplan-Meier curves for DFS/DMFS of the high-risk group and low-risk group classified according to the cutoff-1 criterion using the risk score according to the present invention.

[0162] FIG. 10 shows Kaplan-Meier curves for DFS/DMFS of the high-risk group and low-risk group classified according to the cutoff-2 criterion using the risk score according to the present invention.

[0163] FIG. 11 shows Kaplan-Meier curves for DFS/DMFS of the high-risk group, immune intermediate-risk group and immune low-risk group classified according to both cutoff-1 and cutoff-2 as criteria using the risk score according to the present invention.

[0164] FIG. 12 shows FIG. 11 to which Kaplan-Meier curves for DFS/DMFS of the AL and AI groups (the subgroups classified according to clinical risk evaluation criteria and proliferation gene-risk stratification criteria) are added.

[0165] FIG. 13 shows Kaplan-Meier curves for DFS/DMFS of the high-risk group, the intermediate-risk group and the low-risk group in the HR+/HER2− subtype classified using the risk score model of the present invention. For comparison, FIG. 13 additionally shows Kaplan-Meier curves for DFS/DMFS of the AL and AI groups in the HR+/HER2− subtype (the subgroup classified according to clinical risk assessment criteria and proliferative gene-risk stratification criteria).

[0166] FIG. 14 shows Kaplan-Meier curves for DFS/DMFS of the high-risk group, intermediate-risk group and low-risk group in the HR+/HER2+ subtype classified using the risk score model of the present invention. For comparison, FIG. 14 additionally shows Kaplan-Meier curves for DFS/DMFS of the AL and AI groups in the HR+/HER2+ subtype (the subgroup classified according to clinical risk assessment criteria and proliferative gene-risk stratification criteria).

[0167] FIG. 15 shows Kaplan-Meier curves for DFS/DMFS of the high-risk group, intermediate-risk group and low-risk group of the HR−/HER2+ subtype classified using the risk score model of the present invention. (As described above, the HR−/HER2+ subtype group has been classified into the AH group according to the clinical risk assessment criteria and the proliferation gene-risk stratification criteria).

[0168] FIG. 16 shows Kaplan-Meier curves for DFS/DMFS of the immune high-risk group, immune intermediate-risk group and immune low-risk group in the TNBC (HR−/HER2−) subtype classified using the risk score model of the present invention. For comparison, FIG. 16 additionally shows Kaplan-Meier curves for DFS/DMFS of the AL and AI groups in the TNBC (HR−/HER2−) subtype (the subgroup classified according to clinical risk assessment criteria and proliferative gene-risk stratification criteria).

[0169] FIG. 17 shows Kaplan-Meier curves for overall survival (OS) of the high-risk group and the low-risk group of the Affymetrix microarray platform GPL96 classified using the risk score model of the present invention.

[0170] FIG. 18 shows Kaplan-Meier curves for overall survival (OS) of the high-risk group and the low-risk group of Affymetrix microarray platform GPL570 classified using the risk score model of the present invention.

[0171] FIG. 19 shows Kaplan-Meier curves for DFS of the high-risk group and the low-risk group of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort classified using the risk score model of the present invention.

[0172] FIG. 20 shows Kaplan-Meier curves for OS in the high-risk group and the low-risk group in the HR+/HER2− subtype classified using the risk score model of the present invention.

[0173] FIG. 21 shows Kaplan-Meier curves for OS of the high-risk group and the low-risk group in the HR+/HER2+ subtype classified using the risk score model of the present invention.

[0174] FIG. 22 shows Kaplan-Meier curves for OS of the high-risk group and the low-risk group in the HR−/HER2+ subtype classified using the risk index model of the present invention.

[0175] FIG. 23 shows Kaplan-Meier curves for OS of the high-risk group and the low-risk group in the TNBC (HR−/HER2−) subtype classified using the risk index model of the present invention.

[0176] FIG. 24 shows the results of comparing the performance of the risk score model of the present invention (also referred to as an immune index, indicated by the immune index in the Fig.) in predicting the prognosis of breast cancer (particularly, DFS/DMFS prediction), to other conventional methods (previously, methods for predicting prognosis only with clinical characteristics), by calculating c-index.

BEST MODE FOR INVENTION

[0177] Hereinafter, the present invention will be described in detail. However, the embodiments described below are only to illustrate the present invention, and the scope of the present invention is not limited to the embodiments described below.

Example 1: Data Selection of Breast Cancer Patients

[0178] A. Discovery set: A Public database, National Center for Biotechnology Information Gene Expression

[0179] Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo), was thoroughly to collect five different breast cancer data for analysis.

[0180] The data sets used in this study were strictly selected according to the following criteria: 1) ER (estrogen receptor) status or breast cancer molecular subtype must be confirmed in the clinical data, 2) The patient has not received chemotherapy, 3) The data set has been investigated with the Affymetrix platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array or [HG-U133A] Affymetrix Human Genome U133A Array),

[0181] 4) The data set should include survival information, and include DFS/DMFS (disease free survival/distant-metastasis free survival) or OS (overall survival) information as a more desirable endpoint, 5) The data set should include clinical information on lymph node status, tumor size, patient age and histological grade.

[0182] Finally, microarray data sets to be used in this study were selected from GSE6532, GSE7390, GSE1121, GSE31519, and GSE4922 cohorts (named a discovery set), and they were all investigated with the same platforms, AffymetrixGPL96. A total of 967 patient samples were analyzed.

[0183] The Summary of conventional clinicopathological characteristics for all patients, organized by molecular subtype and cohort, are shown in Table 3 below.

TABLE-US-00003 TABLE 3 Total HR+/HER2− HR+/HER2+ HR−/HER2- HK−/HER2− (n = 967) (n = 619) (n = 99) (n = 47) (n = 202) No. (%) No. (%) No. (%) No. (%) No. (%) Age (years) <50 316 (32.7) 160 (25.8) 42 (42.4) 17 (36.2) 97 (48.0) ≥50 651 (67.3) 459 (74.2) 57 (57.6) 30 (63.8) 105 (25.0) Tumor size (cm) ≤2 522 (54.0) 321 (51.9) 48 (48.5) 14 (29.8) 139 (68.8) 2~5 432 (44.7) 289 (46.7) 43 (48.5) 33 (70.2) 62 (30.7) >5 13 (1.3) 9 (1.4) 3 (3) 1 (0.5) Lymph node status Negative 736 (76.1) 457 (73.8) 75 (75.8) 30 (63.8) 174 (86.1) Positive 181 (18.7) 136 (22.0) 19 (19.2) 11 (23.4) 15 (7.4) NA 50 (5.2) 26 (4.2) 5 (5) 6 (12.8) 13 (6.4) Histologic grade 1 289 (29.9) 164 (26.5) 70 (70.7) 14 (29.8) 41 (20.3) 2 378 (39.1) 353 (57.0) 0 (0.0) 0 (0.0) 25 (12.4) 3 299 (30.9) 102 (16.5) 28 (28.3) 33 (70.2) 136 (67.3) NA 1 (0.1) 0 (0.0) 1 (1) 0 (0.0) 0 (0.0) Total GSE6532 GSE7390 GSE11121 GSE31519 GSE4922 (n = 967) (n = 256) (n = 161) (n = 200) (n = 105) (n = 245) No. (%) No. (%) No. (%) No. (%) No. (%) No. (%) Age (years) <50 316 (32.7) 61 (23.8) 109 (67.7) 47 (23.5) 49 (46.7) 50 (2 .4) ≥50 651 (67.3) 195 (76.2) 52 (32.3) 153 (76.5) 56 (53.3) 195 (79. ) Tumor size (cm) ≤2 522 (54.0) 115 (44.9) 66 (41.0) 112 (56) 105 ( 00) 124 (50.6) 2~5 432 (44.7) 137 (53.5) 95 (59.0) 85 (42.5) 0 (0.0) 115 (46.9) >5 13 (1.3) 4 (1.6) 0 (0.0) 3 (1.5) 0 (0.0) 6 (2.4) Lymph node status Negative 736 (76.1) 176 (68.8) 103 (64.0) 200 (100) 100 (95.2) 157 (64.1) Positive 181 (18.7) 77 (30) 18 (11.2) 0 (0.0) 5 (4.8) 81 (33.1) NA 50 (5.1) 3 (1.2) 40 (24.8) 0 (0.0) 0 (0.0) 7 (2.8) Histologic grade 1 289 (29.9) 88 (34.4) 27 (16.8) 55 (27.5) 35 (33.3) 84 (34.3) 2 378 (39.1) 109 (42.6) 53 (32.9) 110 (55) 0 (0.0) 106 (43.3) 3 299 (30.9) 58 (22.6) 81 (50.3) 35 (17.5) 70 (66.7) 55 (22.4) NA 1 (0.1) 1 (0.4) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) indicates data missing or illegible when filed

[0184] B. Validation set: As a data set for validation, a microarray data sets was selected from GSE21653, GSE42568, and GSE3494 cohorts. In addition, for further validation using other platforms, METABRIC gene expression profiles were analyzed with the same criteria applied to the microarray data set. Data were downloaded via the cBioportal website (http://www.cbioportal.org/index.do) and log 2 normalized prior to analysis.

[0185] Data sets and platforms used in the discovery set and validation in above were summarized in FIG. 2. The patient data in this study were used those classified as 0 (when no metastasis to lymph nodes has occurred) or 1 (when metastasis to lymph node has occurred) whether breast cancer LN- or not according to the cancer metastasis classification TNM (Tumor Node Metastasis) system.

Example 2: Risk Stratification of Patient Breast Cancer Prognosis According to the Existing Method Using Proliferation/Cell Cycle Related Genes

[0186] 2-1. Data Mining

[0187] Based on the information that can identify the molecular subtypes of breast cancer, each patient was classified into four subtypes of breast cancer: HR+/HER2−(ER+ or PR+/HER2−), HR+/HER2+(ER+ or PR+/HER2+), HR−/HER2+(ER−/PR−/HER2+), or TNBC (ER−/PR−/HER2−).

[0188] Data downloaded in Example 1 were log 2 normalized before analysis. Next, in the discovery set, genes exceeding the threshold of the interquartile range were optionally selected to reduce bias. In addition, in order to reduce the non-biological variation present in the selected data set, batch effect correction was performed on the discovery set and validation set using ComBat algorithm, and verified with principal component analysis. After the correction and normalization were performed, the data of each molecular subtype were stratified into 4 risk categories by clinical data and gene risk classification schemes (see Examples 2-4 blow).

[0189] 2-2. Survival Analysis

[0190] The most preferred endpoint used when performing survival analysis is OS(Overall Survival), but OS information is not always available due to temporal limitations. Therefore, when there is a time limit, DFS (Disease Free Survival) or DMFS (Distant-Metastasis Free Survival) is used as a endpoint instead of OS. In this study, OS (overall Survival) and DFS/DMFS were also used as clinical endpoints. Univariate and multivariate analyzes of clinical and genetic variables were performed using Cox proportional hazard regression analysis.

[0191] Multivariate analysis confirmed independent contributions of the predictor variables. In addition, Survival results were graphed using the Kaplan-Meier method and log-rank test, and in differences survival between groups were identified. Statistically significant was estimated when the Log rank p-value<0.05. The above mentioned methods were also used in the same way in the embodiments described later (Examples 3 to 5).

[0192] 2-3. Gene Ontology and Pathway Analysis

[0193] Gene annotation and pathway analysis were performed according to breast cancer subtype. Annotation of gene pathways consists of two parts. First, the most significant genes with p-value of 0.01 or less from DAVID were annotated. For further analysis, using gene annotation package topGO in R version 3.4.3., the pathways of the most significant genes in the regressive analysis were annotated. topGO applies two types of statistics, Fisher's exact test and Kolmogorov-Smirnov test, to calculate gene scores to find the most important pathway. Also, two types of algorithms, the classic method and the elim method can be applied to each statistic.

[0194] In this study, the above mentioned two algorithms were applied to the Kolmogorov-Smirnov test, and the classic Fisher was used to find the most important annotations. Prior to pathway analysis, breast cancer types commonly classified into four subtypes were grouped into three groups: total HR−, HR+/HER+, and HR+/HER−. Because of no statistical difference in survival results between HR−/HER+ and HR−/HER− subtypes (data not shown), so they were combined as HR−.

[0195] The results of gene annotation and pathway analysis were shown in Table 4 below. Table 4 shown that most of genes significantly contributing to survival in the HR+ type were related to cell proliferation and cell cycle regulation, whereas the genes significantly contributing to survival in the HR− type were related to locomotion and immune response.

TABLE-US-00004 TABLE 4 GO.ID Term Annotated Significant Expected Rank in classicKS classicKS elimKS HR+/HER2− GO:0030154 cell differentiation 54 54 54 175 0.77513 0.016 GO:0050793 regulation of developmental process 40 40 40 107 0.32526 0.023 GO:0048869 cellular developmental process 55 55 55 174 0.7526 0.028 GO:2000026 regulation of multicellular organi al d 35 35 35 113 0.33666 0.043 GO:0033554 cellular response to stress 55 55 55 108 0.32814 0.044 GO:0051173 positive regulation of nitrogen compound 57 57 57 36 0.02 73 0.055 GO:0010604 positive regulation of m 60 60 60 42 0. 3029 0.093 GO:0031325 positive regulation of cellular metab 54 54 54 49 0.03221 0.098 GO:0 42981 regulation of apop  process 31 31 31 168 0.71457 0.098 GO:0006366 transcription from RNA polymerase  pr 38 38 38 95 0.24381 0.115 GO:0043067 regulation of programmed cell death 31 31 31 169 0.71457 0.14 GO:0045935 positive regulation of nucl base contai 31 31 31 124 0.38235 0.151 HR+/HER2+ GO:0006260 DNA replication 38 38 38 7 3.50E−05 3.50E−05 GO:0044772 mitotic cell cycle phase trans ion 70 70 70 4 7.90E−06 0.00071 GO:0000070 mitotic sister chromatid segregation 30 30 30 10 0.0011 0.00111 GO:0051301 cell division 71 71 71 12 0.0015 0.0 152 GO:0000082 G  transition of mitotic cell cycle 34 34 34 15 0.0021 0.00215 GO:0007346 regulation of mitotic cell cycle 63 63 63 19 0.0029 0.00287 GO:1901987 regulation of cell cycle phase transitio 49 49 49 22 0.00 0.00805 GO:00 0068 positive regulation of cell cycle proces 33 33 33 23 0.0081 0.008 7 GO:190 647 mitotic cell cycle process 110 110 110 2 4.30E−07 0.00858 GO:0006974 cellular response to DNA damage stimulus 71 71 71 25 0.011 0.01104 GO:19 1990 regulation of mitotic cell cycle phase t 48 48 48 26 0.0112 0.01123 GO:0006281 DNA repair 50 50 50 30 0.0151 0.01511 HR− GO.ID Term Annotated Significant Expected Rank in classicKS classicKS classicFisher GO:0000902 cell morph genesis 32 32 32 172 0.472 1 GO:0001525 genesis 30 30 30 22 0.035 1 GO:0001568 blood vessel development 38 38 38 5 0.011 1 GO:0001775 cell activation 61 61 61 226 0. 1 GO:0001816 cytokine production 36 36 36 302 0.899 1 GO:0001817 regulation of cytokine production 31 31 31 314 0.95 1 GO:0001932 regulation of protein phosphorylation 47 47 47 158 0.426 1 GO:0001934 positive regulation of protein phosph 30 30 30 121 0.35 1 GO:0001944 v ure development 38 38 38 6 0. 1 GO:0002250 ad ptive  response 30 30 30 237 0.647 1 GO:0002252  effector process 55 55 55 288 0.8 5 1 GO:0002376  system process 119 119 119 277 0.797 1 indicates data missing or illegible when filed

[0196]

[0197] 2-4. Risk Stratification Using Genes Related to Proliferate/Cell Cycle

[0198] Based on the pathway analysis, a total of 37 proliferation genes that were related to proliferation and significantly contributing to the survival results in the HR+ group were selected using the following criteria: 1) High variance, 2) Significant result in gene ontology analysis.

[0199] The 37 proliferation genes in above were analyzed to find gene prognostic predictors significantly related to DFS/DMFS (disease free survival/distant-metastasis free survival), and applied to all breast cancer subtypes to find the most significant genes related to cell proliferation through Cox proportional hazard regression analysis.

[0200] Through Cox multivariate proportional hazard regression analysis, a total of 10 genes (BUB1B, UBE2S, RRM2, KIFC1, PTTG1, MELK, CDK1, FOXMI, TRIP13, TACGAP1) were determined to have prognostic ability and independence, and these were the following for genetic risk classification. It was selected as a proliferation/cell cycle regulatory gene.

[0201] For all patient samples, the expression level of each proliferation/cell cycle regulatory gene was classified into two categories “high” or “low” according to the average expression of the gene. If the expression level of 5 or more among the 10 selected genes was classified as a low-risk group for proliferation, otherwise, a high-risk group for proliferation.

[0202] In addition to classifying patients into the molecular subtypes of breast cancer according to gene-risk stratification, patients were classified into clinical high-risk group and low-risk group based on Adjuvant! Online, as shown in Table 5.

[0203] Table. 5 showed the clinical risk assessment for each of the four molecular subtypes of breast cancer, each group being classified according to histological grade, lymph node status, and tumor size.

TABLE-US-00005 TABLE 5 Clinical Risk ER status HER2 statue Grade Nodal status Tumor Size In Mindact ER HER2 well differentiated N- ≤3 cm C-low positive negative 3.1-5 cm C-high 1-3 positive nodes ≤2 cm C-low 2.1-5 cm C-high moderately differentiated N- ≤2 cm C-low 2.1-5 cm C-high 1-3 positive nodes Any size C-low poorly differentiated or N- ≤1 cm C-high undifferentiated 1.1-5 cm C-low 1-3 positive nodes Any size C-high HER2 well differentiated N- ≤2 cm C-low positive OR 2.1-5 cm C-high moderately differentiated 1-3 positive nodes Any size C-low poorly differentiated or N- ≤1 cm C-high undifferentiated 1.1-5 cm C-low 1-3 positive nodes Any size C-high ER HER2 well differentiated N- ≤2 cm C-low negative negative 2.1-5 cm C-high 1-3 positive nodes Any size C-low moderately differentiated N- ≤1 cm C-high OR 1.1-5 cm C-low poorly differentiated or 1-3 positive nodes Any size C-high undifferentiated HER2 well differentiated N- ≤1 cm C-low positive OR 1.1-5 cm C-high moderately differentiated 1-3 positive nodes Any size C-low poorly differentiated or Any Any size C-high undifferentiated

[0204]

[0205] Gene-risk stratification based on proliferation/cell cycle related genes and clinical risk evaluation criteria was subdivided into four risk groups: 1) clinically high-risk/proliferation high risk, 2) clinically high-risk/proliferation low risk, 3) clinically low-risk/proliferation high risk, and 4) clinically low-risk/proliferation low risk.

[0206] However, dividing each breast cancer subtype into the above four risk categories produced insufficient number of samples in each risk category, so each risk category within the breast cancer subtype was combined according to sample size and cox regression results (FIG. 3)

[0207] Specifically, three groups (i.e., AH, AI and AL) were generated for each subtype, clinically high-risk/proliferation high-risk group to classify All high-risk group (AH), clinically high-risk/proliferation low-risk and low-risk/proliferation high-risk to classify All intermediate group hereon, (AI), and clinically low-risk/proliferation low-risk to classify All low-risk group hereon (AL).

[0208] HR+/HER2+ subtype was divided into two risk groups that one was classified into the AH group including clinically high-risk/proliferation high-risk and the others were classified into the AL group involving the rest of them without clinically high-risk/proliferation high-risk.

[0209] All HR−/HER2+ subtype was regarded to the AH group because there was no difference between samples.

[0210] Finally, TNBC subtype was divided into two risk groups that one was classified into the AH group including clinically high-risk/proliferation high-risk, high-risk/proliferation low-risk and clinically low-risk/proliferation high-risk, the other was classified into the Al group including clinically low-risk/proliferation low-risk.

[0211] Patient samples with missing clinical information were excluded from this study. The overall schematics of this study are shown in FIG. 1. In addition, information from the validation set was also classified according to gene-risk stratification and clinical risk evaluation analyzed in a similar manner to discovery set above.

[0212] As shown in FIG. 3, survival results of the subdivided risk subgroups of breast cancer subtype using the Kaplan-Meier method and the log-rank test, also shown in FIG. 4 to FIG. 7.

[0213] A log-rank p-values were p<0.0001 in HR+/HER2− subtype (FIG. 4) and HR+/HER2+ subtype (FIG. 5), TNBC subtype (FIG. 7) was p=0.0018. HR−/HER2+ subtype (FIG. 6) consisted of only the AH groups, so a survival curve could not be estimated. As a result of Cox regression analysis for each risk subgroup, the hazard ratio of the AI group and the AL group compared to the AH group within the HR+/HER2-subtype was 0.613 (p=0.003, 95% CI: 0.444-0.847) and 0.217 (p<0.0001, 95% CI: 0.145-0.327).

[0214] The results within the HR+/HER2+ and TNBC subtype showed similar observations, and the hazard ratio in the AL group versus the AH group were 0.255 (p<0.0001, 95% CI: 0.134-0.483) and 0.377 (p=0.0182, 95% CI: 0.162-0.873-0.08426).

Example 3: Development of New Technology for Predicting the Prognosis of Breast Cancer Using Immune Genes Only

[0215] 3-1 Primary Screening of Immune Genes Related to Predicting the Prognosis of Breast Cancer

[0216] The prognostic value of the immune response genes shown in Table 6 in each subgroup (i.e., risk group) within each breast cancer subtype was analyzed in a similar manner to that described above.

TABLE-US-00006 TABLE 6 Gene code Gene Name TRBV20-1 T cell receptor beta variable 20-1 CCL19 chemokine (C-C motif) ligand 19 CD52 CD52 molecule SRGN serglycin CD3D CD3d molecule, delta (CD3-TCR complex) IGJ immunoglobulin J polypeptide, linker protein for immunoglobulin alpha and mu polypeptides HLA-DRA major histocompatibility complex, class II, DR alpha LOC91316 glucuronidase, beta/immunoglobulin lambda-like polypeptide 1 pseudogene IGF1 insulin-like growth factor 1 (somatomedin C) CYBRD1 cytochrome b reductase 1 TMC5 transmembrane channel-like 5 ALDH1A1 aldehyde dehydrogenase 1 family, member A1 OGN osteoglycin PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) FRZB frizzled-related protein CX3CR1 chemokine (C-X3-C motif) receptor 1 IGFBP6 insulin-like growth factor binding protein 6 GLA galactosidase, alpha LOC96610 BMS1 homolog, ribosome assembly protein (yeast) pseudogene IGLL3 immunoglobulin lambda-like polypeptide 3 ITPR1 inositol 1,4,5-triphosphate receptor, type 1 SERPINA1 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1 EPHX2 epoxide hydrolase 2, cytoplasmic MFAP4 microfibrillar-associated protein 4 RNASET2 ribonuclease T2 CCNG1 cyclin G1 FBLN5 fibulin 5 SORBS2 sorbin and SH3 domain containing 2 CCBL2 cysteine conjugate-beta lyase 2 BTN3A2 butyrophilin, subfamily 3, member A2 TFAP2B transcription factor AP-2 beta (activating enhancer binding protein 2 beta) LTF lactotransferrin ITM2A integral membrane protein 2A HLA-DPB1 major histocompatibility complex, class II, DP beta 1 HLA-DMA HLA-DMA major histocompatibility complex, class II, DM alpha RPL3 ribosomal protein L3 LOC100130100 similar to hCG26659 FAM129A family with sequence similarity 129, member A ELOVL5 ELOVL family member 5, elongation of long chain fatty acids (FEN1/Elo2, SUR4/Elo3-like, yeast) GBP2 guanylate binding protein 2, interferon-inducible RARRES3 retinoic acid receptor responder (tazarotene induced) 3 GOLM1 golgi membrane protein 1 RTN1 reticulon 1 ICAM3 intercellular adhesion molecule 3 LAMA2 laminin, alpha 2 CXCL13 chemokine (C-X-C motif) ligand 13 ZCCHC24 zinc finger, CCHC domain containing 24 CD37 Cluster of Differentiation 37 VTCN1 V-set domain containing T cell activation inhibitor 1 PYCARD PYD and CARD domain containing CORO1A coronin, actin binding protein, 1A SH3BGRL SH3 domain binding glutamic acid-rich protein like TPSAB1 tryptase alpha/beta 1 TNFSF10 tumor necrosis factor (ligand) superfamily, member 10 ACSF2 acyl-CoA synthetase family member 2 TGFBR2 transforming growth factor, beta receptor II (70/80 kDa) DUSP4 dual specificity phosphatase 4 ARHGDIB Rho GDP dissociation inhibitor (GDI) beta TMPRSS3 transmembrane protease, serine 3 DCN decorin LRIG1 leucine-rich repeats and immunoglobulin-like domains 1 FMOD fibromodulin ZNF423 zinc finger protein 423 SQRDL sulfide quinone reductase-like (yeast) TPST2 tyrosylprotein sulfotransferase 2 CD44 CD44 molecule (Indian blood group) MREG melanoregulin GIMAP6 GTPase, IMAP family member 6 GJA1 gap junction protein, alpha 1, 43 kDa IFITM3 interferon induced transmembrane protein 3 (1-8U) BTG2 BTG family, member 2 PIP prolactin-induced protein RPS9 ribosomal protein S9 HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 IMPDH2 IMP (inosine 5′-monophosphate) dehydrogenase 2 TNFRSF17 tumor necrosis factor receptor superfamily, member 17 C14orf139 chromosome 14 open reading frame 139 SPRY2 sprouty homolog 2 (Drosophila) XBP1 X-box binding protein 1 THYN1 thymocyte nuclear protein 1 APOD apolipoprotein D C10orf116 chromosome 10 open reading frame 116 VAV3 vav 3 guanine nucleotide exchange factor FAS Fas (TNF receptor superfamily, member 6) MYBPC1 myosin binding protein C, slow type CFB complement factor B TRIM22 tripartite motif-containing 22 ARID5B AT rich interactive domain 5B (MRF1-like) PTGDS prostaglandin D2 synthase 21 kDa (brain) TGFBR3 transforming growth factor, beta receptor III TNFAIP8 tumor necrosis factor, alpha-induced protein 8 SEMA3C sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C TMEM135 transmembrane protein 135 ARHGEF3 Rho guanine nucleotide exchange factor (GEF) 3 PTGER4 prostaglandin E receptor 4 (subtype EP4) ABCA8 ATP-binding cassette, sub-family A (ABC1), member 8 ICAM2 intercellular adhesion molecule 2 HLA-DQB1 major histocompatibility complex, class II, DQ beta 1 HSPA2 heat shock 70 kDa protein 2 CD27 CD27 molecule ARMCX1 armadillo repeat containing, X-linked 1 POU2AF1 POU class 2 associating factor 1 IGBP1 immunoglobulin (CD79A) binding protein 1 PDE4B phosphodiesterase 4B, CAMP-specific ADH1B alcohol dehydrogenase 1B (class I), beta polypeptide WLS wntless homolog (Drosophila) SUCLG2 succinate-CoA ligase, GDP-forming, beta subunit PGR progesterone receptor STARD13 StAR-related lipid transfer (START) domain containing 13 SORL1 sortilin-related receptor, L(DLR class) A repeats-containing ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide IFT46 intraflagellar transport 46 homolog (Chlamydomonas) SIK3 SIK family kinase 3 LIPT1 lipoyltransferase 1 OMD osteomodulin HBB hemoglobin, beta C3 complement component 3 FGL2 fibrinogen-like 2 PECI peroxisomal D3,D2-enoyl-CoA isomerase RAC2 ras-related C3 botulinum toxin substrate 2 (rho family, small GTP binding protein Rac2) PDZRN3 PDZ domain containing ring finger 3 CXCL12 chemokine (C-X-C motif) ligand 12 DPYD dihydropyrimidine dehydrogenase TXNDC15 thioredoxin domain containing 15 STOM stomatin EMCN endomucin SCGB2A2 secretoglobin, family 2A, member 2 FAM176B family with sequence similarity 176, member B HIGD1A HIG1 hypoxia inducible domain family, member 1A ACSL5 acyl-CoA synthetase long-chain family member 5 RPS24 ribosomal protein S24 RGS10 regulator of G-protein signaling 10 RAI2 retinoic acid induced 2 CNN3 calponin 3, acidic FBXW4 F-box and WD repeat domain containing 4 SEPP1 selenoprotein P, plasma, 1 SLC44A4 solute carrier family 44, member 4 MGP matrix Gla protein ABCD3 ATP-binding cassette, sub-family D (ALD), member 3 SETBP1 SET binding protein 1 APOBEC3G apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G LCP2 lymphocyte cytosolic protein 2 (SH2 domain containing leukocyte protein of 76 kDa) HLA-DRB1 major histocompatibility complex, class II, DR beta 1 SCUBE2 signal peptide, CUB domain, EGF-like 2 DEPDC6 DEP domain containing 6 RPL15 ribosomal protein L15 SH3BP4 SH3-domain binding protein 4 MSX2 msh homeobox 2 CLU clusterin DPT dermatopontin ZNF238 zinc finger protein 238 HBP1 HMG-box transcription factor 1 GSTK1 glutathione S-transferase kappa 1 ZBTB16 zinc finger and BTB domain containing 16 CCDC69 coiled-coil domain containing 69 ALDH2 aldehyde dehydrogenase 2 family (mitochondrial) SLC1A1 solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 1 ARMCX2 armadillo repeat containing, X-linked 2 HMGCS2 3-hydroxy-3-methylglutaryl-CoA synthase 2 (mitochondrial) TSPAN3 tetraspanin 3 FTO fat mass and obesity associated PON2 paraoxonase 2 C16orf62 chromosome 16 open reading frame 62 QDPR quinoid dihydropteridine reductase LRP2 low density lipoprotein receptor-related protein 2 PSMB8 proteasome (prosome, macropain) subunit, beta type, 8 (large multifunctional peptidase 7) HCLS1 hematopoietic cell-specific Lyn substrate 1 FXYD1 FXYD domain containing ion transport regulator 1 OAT ornithine aminotransferase SLC38A1 solute carrier family 38, member 1 MAOA monoamine oxidase A LPL lipoprotein lipase C10orf57 chromosome 10 open reading frame 57 SPARCL1 SPARC-like 1 (hevin) ERAP2 endoplasmic reticulum aminopeptidase 2 PDGFRL platelet-derived growth factor receptor-like RBP4 retinol binding protein 4, plasma LRRC17 leucine rich repeat containing 17 LHFP lipoma HMGIC fusion partner BLNK B-cell linker HBA2 hemoglobin, alpha 2 CST7 cystatin F (leukocystatin) TRAT1 T-cell receptor-associated transmembrane adapter 1 IL21R Interleukin-21 receptor IGHM Immunoglobulin heavy constant mu CTLA4 Cytotoxic T-lymphocyte protein 4 IL2RB Interleukin-2 receptor subunit beta TNFRSF9 Tumor necrosis factor receptor superfamily member 9 CTSW Cathepsin W CCR10 C-C chemokine receptor type 10 GPR18 G Protein-Coupled Receptor 18 CR2 Complement receptor type 2 DOCK10 Dedicator Of Cytokinesis 10 GZMB Granzyme B ITK IL2 Inducible T Cell Kinase LTB Lymphotoxin Beta IGLJ3 Immunoglobulin lambda joining 3 IGLV1-44 Immunoglobulin lambda variable 1-44 AIM2 Absent In Melanoma 2 CXCL9 C-X-C motif chemokine 9 KIAA0125 long non-conding RNA IL2RG Interleukin 2 Receptor Subunit Gamma CD69 Cluster of Differentiation 69 CD55 Cluster of Differentiation 55 TRAF3IP3 TRAF3 Interacting Protein 3 EVI2B Ecotropic Viral Integration Site 2B STAP1 Signal-transducing adaptor protein 1 KLRB1 Killer cell lectin-like receptor subfamily B member 1 PRKCB Protein kinase C beta type GPR171 G Protein-Coupled Receptor 171 PPP1R16B Protein phosphatase 1 regulatory inhibitor subunit 16B SH2D1A SH2 domain-containing protein 1A TNFRSF1B Tumor necrosis factor receptor superfamily member 1B CD48 Cluster of Differentiation 48 BANK1 B-cell scaffold protein with ankyrin repeats 1 LY9 T-lymphocyte surface antigen Ly-9

[0217] A total of 110 immune genes were primary selected in their relevance to MHC-1, MHC-2, T-cells, and B-cells and their relevance to the immune response. Cox regression univariate analysis was performed on the 110 immune response genes, and their significance was observed for each breast cancer molecular subtype. Cox regression analysis was performed in the same manner as in Example 2-2 above. Table 7 showed the 10 most significant immune response genes for each breast cancer subtype.

[0218] In each breast cancer subtype, all AH groups had increased expression of immune response genes significantly related to the positive prognosis. As a result of univariate analysis, in the HR+/HER2− subtype group, 55 immune response genes showed significant p-values (p<0.05), and all had negative coefficient values, and also showed a positive correlation with prolonged survival. In a similar manner, all high-risk groups within HR+/HER2+, HR−/HER2+ and TNBC subtype possessed 96, 30 and 8 immune response genes, respectively, showing significant p-value (p<0.05), which were all had negative coefficients.

[0219] In contrast to the observation in the AH group, the effect of immune response genes was less pronounced in the Al and AL groups. HR+/HER2− subtype in the AI group had no significant survival-related immune response gene and the lowest p-value among all genes was 0.09 or higher. The AL group had several significant genes, but their hazard ratio was not associated with positive DFS/DMFS results.

[0220] Based on the results of Cox regression analysis, it focused to AH group to further investigate genes with prognostic predictive ability.

TABLE-US-00007 TABLE 7 coef hr se(coef) z pvalue HR+/HER2− CD69 −0.68986 0.501646 0.211284 −3.26509 0.001094 CD55 −1.02617 0.358375 0.323082 −3.1762 0.001492 TRAF3IP3 −0.93552 0.392382 0.311055 −3.00757 0.002633 EVI2B −0.91907 0.398891 0.30849 −2.97923 0.00289  IL21R −0.80724 0.44609 0.273252 −2.95418 0.003135 IGHM −0.42671 0.652655 0.148793 −2.86779 0.004134 IGJ −0.38369 0.681341 0.135338 −2.83506 0.004582 CR2 −0.46309 0.629334 0.164106 −2.82192 0.004774 GZMB −0.553 0.575221 0.20029 −2.761 0.005762 STAP1 −0.59319 0.552562 0.218993 −2.70871 0.006755 HR+/HER2+ KLRB1 −1.52862 0.216835 0.325476 −4.69656 2.65E−06 PRKCB −2.19827 0.110995 0.468355 −4.6936 2.68E−06 CD37 −1.93956 0.143767 0.413562 −4.68989 2.73E−06 GPR171 −2.04521 0.129353 0.436535 −4.6851 2.80E−06 CD3D −1.92875 0.14533 0.417865 −4.61572 3.92E−06 PPP1R16B −1.67262 0.187754 0.365964 −4.57047 4.87E−06 ITK −2.57119 0.076444 0.564613 −4.5539 5.27E−06 SH2D1A −2.41604 0.089274 0.531415 −4.54643 5.46E−06 TNFRSF1B −3.84986 0.021283 0.847878 −4.54058 5.61E−06 CD48 −2.60977 0.073551 0.582633 −4.47927 7.49E−06 HR−/HER2+ BANK1 −0.94469 0.3888 0.261254 −3.61599 0.000299 GIMAP6 −2.0162 0.13316 0.644425 −3.12868 0.001756 CD69 −1.61401 0.199087 0.542267 −2.97642 0.002916 GPR18 −0.94085 0.390294 0.335457 −2.80469 0.005036 LY9 −0.99188 0.370877 0.353694 −2.80436 0.005042 VNN2 −1.05607 0.347819 0.388099 −2.72115 0.006506 TCL1A −2.0106 0.133909 0.742011 −2.70966 0.006735 CYTIP −1.61108 0.199672 0.606694 −2.6555 0.007919 CTSW −0.89989 0.406614 0.351074 −2.56325 0.01037  PTPRC −1.2287 0.292673 0.483899 −2.53916 0.011112 TNBC PDCDILG2 −0.67776 0.507751 0.258882 −2.61804 0.008844 LTA −1.2035 0.300141 0.484417 −2.48444 0.012976 IGLV1-44 −0.39441 0.674078 0.1651 −2.38891 0.016898 CCR10 −0.75399 0.470485 0.329995 −2.28486 0.022321 TNFRSF9 −0.8434 0.430247 0.370424 −2.27684 0.022796 GPR18 −0.47547 0.621591 0.218 −2.18107 0.029178 IGLJ3 −0.49076 0.612164 0.232956 −2.10664 0.035148 IGHG1 −0.54498 0.579854 0.268435 −2.03021 0.042335 IGHM −0.29906 0.741512 0.153638 −1.94655 0.051589 CD19 −0.49133 0.611815 0.254689 −1.92912 0.053716

[0221] 3-2. Screening and Selection of Main Immune Response Genes

[0222] Using a Lasso regression analysis, we tried to further selected significant immune response genes related to patient survival in each breast cancer subtype. First, Lasso feature selection method was used to select the most significant immune response genes in relation to DFS/DMFS, and applied to ‘coxnet’ of R version 3.4.3. to find the optimal lambda value by 10,000 fold cross-validation. And then the active covariate was found by the Lasso method. As mentioned above, the most significant genes were selected by performing Lasso regression analysis, and these results were verified through Cox proportional hazard univariate analysis.

[0223] Further details, the AH group was integrated in each breast cancer subtype. Patient data with missing or ambiguous clinical information were excluded from this analysis and subsequently analyzed during the development of prognostic models. 9 active genes (CTLA4, CTSW, DOCK10, GPR18, IGHM, IL21R, IL2RB, TNFRSF9, and TRAT1) that negatively affect hazard were selected by Lasso regression analysis, and were shown in Table 8 below. In addition, 5 genes (TRAT1, IGHM, IL21R, GZMB, GPR18) by Cox regression analysis that had a significant effect (p<0.0001) on hazard was discovered, and the analysis results of these genes were shown in Table 9 below.

[0224] Finally, 5 genes (TRAT1, IL21R, IGHM, CTLA4, IL2RB) with negative coefficient value of less than −0.05 were selected (See Table 8).

TABLE-US-00008 TABLE 8 Gene Coefficient TRAT1 −0.13118865 IL21R −0.10504567 IGHM −0.0997505 CTLA4 −0.09963025 IL2RB −0.08664438 INFRSF9 −0.04891361 CTSW −0.04188042 CCR10 −0.01014675 GPR18 −0.00497377 CR2 −0.00196256 DOCK10 0.240217798

TABLE-US-00009 TABLE 9 coef hr se(coef) z pvalue TRAT1 −0.38483917 0.68056 0.090824 −4.23721 2.26E−05 IGHM −0.36913285 0.691334 0.08911 −4.14242 3.44E−05 IL21R −0.63111572 0.531998 0.155136 −4.06814 4.74E−05 GZMB −0.43362517 0.648155 0.109901 −3.94561 7.96E−05 GPR18 −0.51640599 0.596661 0.132572 −3.89528 9.81E−05 CTSW −0.424037 0.6544 0.110839 −3.82572 0.00013 EVI2B −0.69469314 0.499228 0.184028 −3.77492 0.00016 CORO1A −0.65641984 0.518705 0.174807 −3.75512 0.000173 CTLA4 −0.50054211 0.606202 0.133582 −3.74706 0.000179 ITK −0.61326229 0.541581 0.163868 −3.74242 0.000182 LTB −0.50805218 0.601666 0.138251 −3.67486 0.000238 IGLJ3 −0.5058777 0.602976 0.137725 −3.67311 0.00024 IGLV1-44 −0.36467637 0.694421 0.099401 −3.66876 0.000244 AIM2 −0.70455447 0.494329 0.192936 −3.65175 0.00026 CXCL9 −0.32522115 0.722368 0.091129 −3.56895 0.000358 IL2RB −0.76753583 0.464155 0.216092 −3.5519 0.000382 CXCL13 −0.23298293 0.792167 0.065837 −3.53879 0.000402 KIAA0125 −0.8382797 0.432454 0.237175 −3.53444 0.000409 IL2RG −0.58234889 0.558585 0.165644 −3.51567 0.000439

[0225] 3-3. Production of a Risk Score Calculation Model for Predicting the Prognosis of Early Breast Cancer

[0226] A model for predicting the prognosis of breast cancer was created by combining the five immune genes selected in Example 3-2. The inventors of the present invention have confirmed that a breast cancer prognosis risk score could be calculated by performing a linear combination of the expression value of each of the five immune genes selected above and Cox Regression estimates (used as coefficients). The Cox regression estimate of each gene is shown in Table 10 below.

TABLE-US-00010 TABLE 10 Cox Regression estimate Cox Regression Gene 95% confidence interval point estimate TRAT1 −0.567144, −0.1952896 −0.3812 IL21R −0.9759746, −0.3412672 −0.6586 CTLA4 −0.7454524, −0.2010003 −0.4732 IGHM −0.5428339, −0.1855019 −0.3642 IL2RB −1.146983, −0.266771 −0.7069 Lymph node 0.3910642, 1.013551 0.7023 * 2 infiltration status

[0227] In particular, in order to include information on clinical variables for more accurate prediction, Cox univariate and multivariate analysis were performed on the clinical variables, as a result, among the clinical variables, it was confirmed that the breast cancer infiltration status in the lymph nodes (herein, abbreviated as ‘lymph nodes status’) had the most significant effect on survival as an independent prognostic factor (data not shown). Accordingly, a risk score calculation formula for predicting breast cancer prognosis was produced as follows using the Cox regression estimate for the lymph node state. As described below, the risk score calculated by the present invention was genetic information and was also referred to as an ‘immune index’ in the present specification because it included only immune genes.

risk score={(−0.3812*χ.sub.TRAT1)+(−0.6586*χ.sub.IL21R)+(−0.3642*χ.sub.IGHM)+(−0.4732*χ.sub.CTLA4)+(−0.7069*χ.sub.IL2RB)}+(0.7023*2*LN)  [Formula 3]

[0228] In Formula 3, x is the expression value of a gene indicated by a subscript, and N is an integer indicating the presence of LN.

Example 4: Confirmation of Prognostic Performance of Breast Cancer Prognostic Model of the Present Invention Using Immune Response Genes

[0229] In Example 3-3 above, the risk index of each patient in the discovery set was calculated according to the risk index calculation formula prepared. Based on the risk index (immunity index), patient samples within the AH group were further stratified into specific risk groups. In the present invention, the performance of the risk score was tested in two parts: 1) Hazard index as a continuous variable, 2) Risk index based on the optimal cutoff point derived using rank statistics, maximally selected from the R version 3.4.3.′ survminer′ package by the bootstrapping method.

[0230] We hypothesized that a lower (more negative) risk index was associated with a reduced chance of recurrence as well as prolonged survival.

[0231] Table 11 below showed the results of univariate analysis and multivariate analysis performed in relation to the risk index of the present invention, respectively. Continuous risk index based on univariate analysis was significantly and highly associated with relapse result (p<0.0001, Table 11).

[0232] Statistical significance was also confirmed in multivariate analysis of risk index and clinical factors, and as the risk index increased, the risk index was the most prominent variable associated with recurrence, and the hazard ratio of 1.46 (p<0.0001, 95% CI: 1.30-1.65) appeared (Table 11). These results suggested that a lower risk score is associated with a reduced chance of recurrence as well as long-term survival.

TABLE-US-00011 TABLE 11 Hazard Ratio 95% CI P value Univariate Analysis Number of patients n = 386 Event = 181 Risk Score: Risk Optimal High 1.00 Intermediate 0.42 0.29-0.60 <0.0001 Low 0.17 0.10-0.29 <0.0001 Clinical Variables: Lymph node infiltration 0 1.00 1 2.02 1.48-2.76 <0.0001 Histological grade High 1.00 Low&Intermediate 1.24 0.93-1.67 0.146 Tumor size A 1.00 B 0.75 0.55-1.02 0.0679 Age A 1.00 B 1.10 0.81-1.48 0.55 Multivariate Analysis Number of patients n = 386 Event = 181 Risk Score: Risk Optimal High 1.00 Intermediate 0.49 0.32-0.73 0.0004 Low 0.21 0.12-0.37 <0.0001 Clinical Variables: Lymph node infiltration 0 1.00 1 1.31 0914-1.88  0.14044

[0233] Two optimal points were selected through bootstrapping of the maximally selected rank statistics. The optimal cutoff point of the risk index according to the model of the present invention was obtained by bootstrapping the most selected statistics in the ‘survminer’ package (R version 3.4.3.).

[0234] FIG. 8 shows two cutoff points identified by the bootstrapping method. When the risk index of patients is normalized by the bootstrapping method and expressed as a distribution, based on the reliability of 85%, the cut-off value for the 2.5 percentile was set as cutoff-1, and 97.5 the cutoff value for the quartile was cutoff-2. As a result, the two cutoff values calculated by the bootstrapping method were −9.4 and −7.1, and if it was less than −9.4, it was classified as low-risk, if it was between −9.4 and −7.1, it was classified as intermediate risk, and −7.1 if it is greater than that, it is classified as high-risk.

[0235] Cutoff-2 (−9.401574213, rounded to −9.4) stratified the low-risk group and the high-risk group, and the low-risk group had a hazard ratio of 0.35 (p=0.0001, 95% CI: 0.25-0.50) (FIG. 9).

[0236] The group stratified by cutoff −1 (−7.061178192, rounded to −7.1) revealed a significant difference in recurrence rate, and a hazard ratio of 0.35 (p<0.0001, CI: 0.22-0.56) (FIG. 10).

[0237] Two optimal cutoff points (i.e., cutoff-1 and cutoff-2) were used together, and those classified differently as high or low risk according to the two cutoff points were classified as an intermediate group (Table 11 and FIG. 8). By applying this to the risk score of the present invention, three risk groups were created based on the risk index: immune high-risk, immune intermediate-risk, and immune low-risk (Table 11 and FIG. 11). FIG. 11 shows survival curves of a discovery set stratified into three risk groups. All three risk groups showed statistically significant differences. Compared to the high-risk group, the hazard ratio of the intermediate-risk group was 0.42 (p<0.0001, CI: 0.29-0.56), and the hazard ratio of the low-risk group was 0.17 (p<0.0001, CI: 0.10-0.29).

[0238] 5-year survival rate was 90.9% in the low-risk group, 56.4% in the low-risk group, 32.5% in the high-risk group. In addition, 10-year survival rate decreased to 73.4% in the low-risk group, 51.3% in the intermediate-risk group, and 14.1% in the low-risk group. FIG. 12 is basically the same as FIG. 11, but showed the survival curves for the AL group and the AI group, which were excluded in the development of the prognostic prediction model (formular) of the present invention. As shown in FIG. 12, there was no statistical difference between the immune low-risk group and the AL&AI group classified according to the judgment using the risk index of the present invention, while comparing the immune intermediate-risk group and the immune low-risk group it showed that a statistically significant difference appears.

[0239] In order to find out whether the risk index according to the invention has independence for the prediction of breast cancer recurrence, the risk index was verified through multivariate analysis, which is shown in Table 11 above.

[0240] When adjusted for conventional clinicopathological parameters, the risk index (immunological marker) showed statistical significance by multivariate analysis.

[0241] In addition, each molecular subtype of breast cancer (HR+/HER2−, HR+/HER2+, HR−/HER2+, TNBC) was tested by applying the risk index model of the present invention. The survival curves of the intermediate-risk group and the immune-low risk group are shown in FIGS. 13 to 16.

[0242] Excluding FIG. 15 (since all HR−/HER2+ subtype groups were classified as AH, see examples 2-4 above), FIG. 13(HR+/HER2−), FIG. 14(HR+/HER2+), and FIG. 16(TNBC), together with the survival curves for the AL & AI groups, the survival curve of the AL & AI group showed a tendency similar to that of the immune low-risk group according to the risk index of the present invention. As shown in FIGS. 13 to 16, the risk index (immune index) of the present invention was statistically significant in all four molecular subtypes of breast cancer (p<0.05).

Example 5: Verification of Prognostic Performance of Breast Caner Prognostic Model of the Present Invention Using Immune Genes

[0243] Unlike the discovery set used in above embodiments, in order to expand the scope of application of the breast cancer prognosis prediction model (risk index calculation model) of the present invention, cohorts on various other platforms are used in the present invention: The breast cancer prognostic risk index model of the present invention was tested by a total of three different test set (i.e. validation set): two different microarray platform sets and another validation set using METABRIC data. As a microarray platform set, GSE3494, which was selected as the first validation set, was the same platform as the cohort of the discovery set (Affymetrix GPL96). The second validation set consisted of two cohorts, GSE21653 and GSE42568 (Affymetrix GPL570).

[0244] In order to stratify patients within the AH group into immune low-risk groups and immune high-risk groups by applying the risk index of the present invention, the optimal cutoff value −7.1 (cutoff-1, see Example 4 above) was applied to the validation set. In the validation set, there was no sample showing a risk index as low as −9.4 (cutoff-2, see Example 4 above), which was thought to be due to the difference in whether patients performed chemotherapy between the discovery set and the validation set.

[0245] FIGS. 17, 18, and 19 showed survival curves in three validation sets of Affymetrix GPL96, Affymetrix GPL570, and METABRIC, respectively, and showed that there was a significant difference in recurrence and survival between the low-risk group and the high-risk group. FIG. 17 showed the OS (overall survival) difference between the immune high-risk group and the immune low-risk group and the immune low-risk group defined by the risk index (immunity index) of the present invention in the GSE3494 cohort, wherein in hazard ratio in the immune low-risk group was 0.36 (p=0.0339, CI: 0.14-0.92). FIG. 18 showed the possibility of recurrence between the immune high-risk group and the immune low-risk group defined by the risk index of the present invention in the validation set consisting of GSE21653 and GSE42568, wherein the hazard ratio in the immune low-risk group was 0.24 (p=0.0137, CI: 0.07-0.74). In both validation sets, the risk index (immunity index) successfully classified the immune low-risk group and the immune high-risk group, and showed a statistical difference in survival results.

[0246] In addition, in the first validation set (GSE3494), the 5-year overall survival rates of the low-risk group and high-risk group were 90.0% and 60.9%, respectively. In the second validation set (GSE42683 and GSE21653), the low-risk group and the year-DFS of the high-risk group was 89.7% and 50.0%, respectively. In the first validation set, the 10-year overall survival rates of the low-risk and high-risk groups were 75.0% and 50.8%, respectively. In second validation set, the recurrence rates of the low-risk and high-risk groups were 798 and 33.7%, respectively. In addition, as a result of performing univariate analysis and multivariate analysis on each validation set, the risk index of the present invention was found to be the largest variable in predicting prognosis after adjustment (Tables 12 and 13). Taken together, based on the results from the microarray validation sets, the risk model for predicting breast cancer prognosis of the present invention demonstrated robustness (robustness or robustness) in predicting overall survival (OS) and recurrence (p<0.05).

TABLE-US-00012 TABLE 12 Hazard Ratio 95% CI F value Univariate Analysis (GSE3494) Number of patients n = 86 Events = 33 Risk Score: Continous As score increases 2.24 1.41-3.37 0.000664 Risk Optimal High 1.00 Low 0.36 0.14-0.92 0.0339 Clinical Variables: Lymph node infiltration 0 1.00 1 2.74 1.30-5.80 0.00824 Histological grade High 1.00 Low&Intermediate 1.03 0.52-2.04 0.937 Tumor size A 1.00 B 2.36 0.83-6.73 0.107 Age A 1.00 B 1.39 0.66-2.93 0.382 Multivariate Analysis (GSE3494) Number of patients n = 86 Events = 33 Risk Score: Continous As score increases 2.73 1.13-6.58 0.0252 Clinical Variables: Lymph node infiltration 0 1.00 1 0.68 0.17-2.88 0.604

[0247] The classification in the table is based on the classification of clinical variables commonly used in breast cancer (AGE: 50 or greater=A otherwise B (50<=A 50>B; Size: B>2 cm otherwise A; Histological grade: .fwdarw.1: low, 2: intermediate, 3: high).

TABLE-US-00013 TABLE 13 Univariate Analysis (GSE42563 & GSB21563) Number of patients n = 130 Risk Score: Continous As score increases 2.47 1.42-4.30 0.00139 Risk Optimal High 1.00 Low 0.24 0.07-0.74 0.0137 Clinical Variables: Lymph node infiltration 0 1.00 1 2.41 1.38-4.22 0.00198 Histological grade High 1.00 Low&Intermediate 0.65 0.35-1.21 0.173 Tumor size A 1.00 B 0.89 0.35-2.22 0.797 Age A 1.00 B 0.83 0.48-1.45 0.52 Multivariate Analysis (OSE42568 & GSE21653) Hazard Ratio 99% CI P value Number of patients n = 130 Risk Score: Continous 1.00 As score increase 2.30 1.31-4.04 0.00384 Clinical Variables: Lymph node infiltration 0 1.00 1 2.19 1.25-3.83 0.00619

[0248] Finally, using overall survival as a primary endpoint, the risk index of the present invention was verified by the METABRIC cohort. Because of the wealth of clinical information, including adjuvant chemotherapy, we were able to select only patients who did not receive adjuvant chemotherapy, as we did in the discovery set. A total of 370 patients in the METABRIC cohort were analyzed by our risk index model. However, since only three genes (TRAT1, IL21R and CTLA4) among the five genes constituting the risk index model of the present invention were found in the METABRIC data set, and as a result, excluding 2 genes (IGHM and IL2RB), coefficients for the three genes were obtained from the METABRIC dataset, and Cox coefficient values were changed and applied using these coefficients. In the changed result, cox regression values were newly obtained and applied. β.sub.TRAT1 was calculated as −0.6414, β.sub.IL21R was −0.2797, β.sub.CTLA4 was −0.3790, and F was calculated as 0.6208.

TABLE-US-00014 TABLE 14 Cox Regression estimate Cox Regression Gene 95% confidence interval point estimate TRAT1 −1.06659, −0.2163024 −0.6414 IL21R −0.5429339, −0.01642154 −0.2797 CTLA4 −0.5934638, −0.1644545 −0.3790 Lymph node status 0.311146, 0.9303696 0.6208

[0249] As a result of the survival analysis performed in the METABRIC cohort, the risk index model of the present invention classified by the optimal cutoff point preserved statistical significance (Table 14). As shown in FIG. 19, the Cox regression analysis performed on the METABRIC validation set confirmed that there was significant statistical significance in the immune low-risk group and high-risk group classified according to the optimal cutoff value based on the risk index model of the present invention (FIG. 19).

TABLE-US-00015 TABLE 15 Hazard Ratio 99% CI P value Univariate Analysis Number of patients n = 370 Events = 250 Risk Score: Continous As score increase 1.7 1.36-2.13 <0.0001 Risk Optimal High 1.00 Low 0.43 0.30-0.61 <0.0001 Clinical Variables: Lymph node infiltration 0 1.00 1 1.86 1.37-2.53 <0.0001 Histological grade High 1.00 Low&Intermediate 1.14 0.88-1.50 0.3080 Tumor size A 1.00 B 1.74 1.32-2.30 0.0001 Age A 1.00 B 0.86 0.56-1.32 0.4890 Multivaraite Analysis Number of patients n = 370 Events = 250 Risk Score: High 1.00 Low 0.50 0.35-0.73 0.0003 Clinical Variables: Lymph node infiltration 0 1.00 1 1.22 0.84-1.77 0.2886 Tumor size A 1.00 B 1.40 1.02-1.92 0.0361 Age A 1.00 B 0.99 0.64-1.33 0.9994

[0250] In addition, in the METABRIC data set, the risk index of the present invention showed significance for OS (overall survival), and showed the strongest prognostic performance even after adjusting for other variables (see Table 15). The 5-year survival rate was 97.0% in the low-risk group and 72.1% in the high-risk group. The 10-year survival rate was 83.3% in the low-risk group and 51.2% in the high-risk group.

[0251] Finally, the risk index model of the present invention was applied to all breast cancer subtypes HR+/HER2−, HR=/Her2+, HR−/HER2+, and TNBC in the METABRIC data set, and the results are shown in FIGS. 20, 21, 22, and 23. As shown in FIG. 11 to FIG. 23, significance was shown in all breast cancer subtypes when the risk index model of the present invention was applied.

Example 6: Comparative Evaluation of Breast Cancer Prognosis Prediction Model Using C-Index

[0252] Using Harrell's Concordance Index (C-index), the performance of the existing prognosis prediction method based on other clinical variables and the risk index model for predicting breast cancer prognosis of the present invention were compared (FIG. 24). The concordance index (C-index) was calculated from the ‘survcomp’ package in R version 3.4.3. The concordance index (C-index) is a standard measure to evaluate the performance of predictive models in survival analysis.

[0253] As shown in the result of C-index in FIG. 24, compared with traditional clinicopathological variables such as lymph node status (C-index: 0.57), tumor size (C-index: 0.56), histological grade (C-index: 0.52), age (C-index: 0.50), the risk index (also referred to as immune index) in the present invention showed the highest C-index of 0.64. These results verify the independence of the risk index in the present invention as a predictive prognostic indicator of breast cancer recurrence and metastasis, and the risk index model of the present invention has better prognostic predictive performance than existing clinical pathological variables.

INDUSTRIAL APPLICABILITY

[0254] As described above, the present invention relates to a method for predicting the prognosis of patients' breast cancer and, more particularly, to a method for predicting the prognosis of breast cancer by combining immune-related genes. The present invention may not only be applied to all patients with breast cancer regardless of breast cancer molecular subtypes, but also predict the prognosis of patients' breast cancer without information on proliferation genes by using a combination of immune-related genes to predict the prognosis of breast cancer according to the present invention. Therefore, the present invention has great industrial applicability.