Method for determining breast cancer prognosis

Abstract

To provide a novel method for determining breast cancer prognosis.SOLUTION: Provided is a method for determining breast cancer prognosis using all of the following 27 gene groups as indicators: ASPM (SEQ ID NO: 1), BCL11A (SEQ ID NO: 2), BF930764 (SEQ ID NO: 3), BIRC5 (SEQ ID NO: 4), C10orf3 (SEQ ID NO: 5), CCNB2 (SEQ ID NO: 6), CDC45 L (SEQ ID NO: 7), CDCA8 (SEQ ID NO: 8), CENPF (SEQ ID NO: 9), FLJ10719 (SEQ ID NO: 10), FLJ11280 (SEQ ID NO: 11) ), FLJ14399 (SEQ ID NO: 12), HIS1 (SEQ ID NO: 13), HSPC150 (SEQ ID NO: 14), KIF2C (SEQ ID NO: 15), LOC51161 (SEQ ID NO: 16), MGC7036 (SEQ ID NO: 17), MKNK2 (SEQ ID NO: 18), PKMYT1 (SEQ ID NO: 19), PLK (SEQ ID NO: 20), PRC1 (SEQ ID NO: 21), PTP4A2 (SEQ ID NO: 22), RPS27 L (SEQ ID NO: 23), STMN1 (SEQ ID NO: 24), SULF2 (SEQ ID NO: 25), TGS (SEQ ID NO: 26), and UBE2C (SEQ ID NO: 27).SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a method for determining the prognosis of breast cancer. [Background technology]

[0002] Structural mutations in the TP53 gene are known as an independent prognostic factor for breast cancer, but because there is no simple and reliable diagnostic method, they have not been introduced into clinical practice. The present inventors have developed a gene expression profile (TP53 signature) that can predict the presence or absence of loss-of-function mutations in the TP53 gene by comprehensive gene expression analysis, and obtained a patent (Patent Document 1). In the method described in Patent Document 1, the TP53 signature status of a tumor is determined by calculating a correlation coefficient between the gene expression profiles of the TP53 gene mutant and wild type. In order to calculate the correlation coefficient, it is necessary to standardize the gene expression data of the tumor, and expression data of a large number of breast cancer cases to be compared are required for standardization. Since a large number of cases to be compared are required, the standardization algorithm is complicated, and there is a possibility that excessive data correction may occur due to standardization, a diagnostic method that improves these points was investigated. As a result, a method was reported in which the ratio of the sum of the expression levels of genes whose expression is increased in the TP53 gene mutant type to the sum of the expression levels of genes whose expression is decreased is calculated, and a TP53 signature mutant type is diagnosed when the ratio is equal to or greater than the cutoff value, and a TP53 signature wild type is diagnosed when the ratio is equal to or less than the cutoff value (Non-Patent Document 1).

[0003] However, this method had issues with its diagnostic accuracy in ER-positive patient groups, Stage I patient groups, lymph node-negative patient groups, Grade 1 patient groups, patient groups over 50 years of age, and patient groups with Ki-67≧10%. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4370409 [Non-patent literature]

[0005] [Non-Patent Document 1] Oncotarget 9: 14193-14206. 2018 Summary of the Invention [Problem to be solved by the invention]

[0006] An objective of the present invention is to provide a new method for determining the prognosis of breast cancer. Another objective of the present invention is to provide a method capable of diagnosing the prognosis with high accuracy, particularly in a group of breast cancer patients with a relatively low risk of recurrence. [Means for solving the problem]

[0007] Under such circumstances, the present inventors have found that the above-mentioned problems can be solved in a very simple manner by focusing on a specific gene among the wide variety of genes whose expression levels are affected by the presence or absence of a pathological mutation in the TP53 gene, and have thus completed the present invention. Accordingly, the present invention provides the following: Item 1. A method for determining the prognosis of breast cancer using all of the following 27 gene groups as indicators: ASPM (SEQ ID NO: 1) BCL11A (SEQ ID NO:2) BF930764 (SEQ ID NO: 3) BIRC5 (SEQ ID NO: 4) C10orf3 (SEQ ID NO:5) CCNB2 (SEQ ID NO:6) CDC45L (SEQ ID NO: 7) CDCA8 (SEQ ID NO: 8) CENPF (SEQ ID NO: 9) FLJ10719 (SEQ ID NO: 10) FLJ11280 (SEQ ID NO: 11) FLJ14399 (SEQ ID NO: 12) HIS1 (SEQ ID NO: 13) HSPC150 (SEQ ID NO: 14) KIF2C (SEQ ID NO: 15) LOC51161 (SEQ ID NO: 16) MGC7036 (SEQ ID NO: 17) MKNK2 (SEQ ID NO: 18) PKMYT1 (SEQ ID NO: 19) PLK (SEQ ID NO: 20) PRC1 (SEQ ID NO:21) PTP4A2 (SEQ ID NO: 22) RPS27L (SEQ ID NO: 23) STMN1 (SEQ ID NO:24) SULF2 (SEQ ID NO:25) TGS (SEQ ID NO:26) UBE2C (SEQ ID NO:27) Item 2. The method according to Item 1, wherein the prognosis is judged to be good when the TP53 signature score calculated by the following formula is less than a cutoff value: TP53 signature score = [sum of logarithms of expression values ​​of gene group A below] / [sum of logarithms of expression values ​​of gene group B below] Gene group A ASPM (SEQ ID NO: 1) BCL11A (SEQ ID NO:2) BF930764 (SEQ ID NO: 3) BIRC5 (SEQ ID NO: 4) C10orf3 (SEQ ID NO:5) CCNB2 (SEQ ID NO:6) CDC45L (SEQ ID NO: 7) CDCA8 (SEQ ID NO: 8) CENPF (SEQ ID NO: 9) FLJ10719 (SEQ ID NO: 10) HSPC150 (SEQ ID NO: 14) KIF2C (SEQ ID NO: 15) PKMYT1 (SEQ ID NO: 19) PLK (SEQ ID NO: 20) PRC1 (SEQ ID NO:21) STMN1 (SEQ ID NO:24) TGS (SEQ ID NO:26) UBE2C (SEQ ID NO:27) Gene group B FLJ11280 (SEQ ID NO: 11) FLJ14399 (SEQ ID NO: 12) HIS1 (SEQ ID NO: 13) LOC51161 (SEQ ID NO: 16) MGC7036 (SEQ ID NO: 17) MKNK2 (SEQ ID NO: 18) PTP4A2 (SEQ ID NO: 22) RPS27L (SEQ ID NO: 23) SULF2 (SEQ ID NO:25) Item 3. The method according to Item 1 or 2, wherein the group of genes is derived from a sample prepared from breast cancer tissue or breast cancer cells collected from a breast cancer patient. Effect of the Invention

[0008] According to the present invention, a novel method for determining the prognosis of breast cancer can be provided. The present invention also provides a method capable of diagnosing the prognosis with high accuracy in a patient group with a relatively low risk of recurrence. [Brief description of the drawings]

[0009] [Figure 1] Top left: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature wild type group (wt group) and the TP53 signature mutant type (mt group) in 216 cases of stage I-II breast cancer. Top right: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature' wild type group (wt group) and the TP53 signature' mutant type (mt group) in 216 cases of stage I-II breast cancer. Bottom left: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature wild type group (wt group) and the TP53 signature' mutant type (mt group) in 148 cases of ER positive. Bottom right: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature' wild type group (wt group) and the TP53 signature' mutant type (mt group) in 148 cases of ER positive. [Diagram 2]Top left: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature wild type group (wt group) and TP53 signature mutant type (mt group) for 115 Stage I cases. Top right: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature' wild type group (wt group) and TP53 signature' mutant type (mt group) for 115 Stage I cases. Bottom left: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature wild type group (wt group) and TP53 signature' mutant type (mt group) for 154 lymph node metastasis-negative cases. Bottom right: A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature' wild type group (wt group) and TP53 signature' mutant type (mt group) for 154 lymph node metastasis-negative cases. [Diagram 3] Top left: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature wild type group (wt group) and TP53 signature mutant type (mt group) for 55 grade 1 cases. Top right: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature' wild type group (wt group) and TP53 signature' mutant type (mt group) for 55 grade 1 cases. Bottom left: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature' wild type group (wt group) and TP53 signature' mutant type (mt group) for 149 cases aged 51 years or older. Bottom right: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature' wild type group (wt group) and TP53 signature' mutant type (mt group) for 149 cases aged 51 years or older. [Figure 4]Left: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature wild type group (wt group) and the TP53 signature mutant type (mt group) in 152 patients with Ki-67 of 10% or more. Right: A graph summarizing the recurrence-free survival rate and survival time for the TP53 signature' wild type group (wt group) and the TP53 signature' mutant type (mt group) in 152 patients with Ki-67 of 10% or more. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present invention relates to a method for determining the prognosis of breast cancer using a specific group of genes as an index. In the present invention, the term "gene" includes not only structural genes that define the primary structure of proteins, tRNA, rRNA, etc., but also regions on nucleic acids that have specific control functions, such as promoters and operators, unless otherwise specified. Therefore, in the present invention, the term "gene" refers to regulatory regions, coding regions, exons, and introns without distinction, unless otherwise specified. In addition, "structural genes" include genes in which silent mutations have been made in the original DNA sequence. In the present invention, the term "gene" also includes nucleic acid molecules such as siRNA that interfere with gene expression.

[0011] In this specification, "nucleic acid" is synonymous with nucleotide, oligonucleotide, and polynucleotide, and may be any of DNA, RNA, and DNA-RNA hybrids. These may be double-stranded or single-stranded, and when a nucleic acid molecule having a certain sequence is mentioned, it is meant to comprehensively mean a nucleic acid molecule (or nucleotide, oligonucleotide, and polynucleotide) having a complementary sequence, unless otherwise specified. These nucleic acid molecules may be circular or linear, and may be synthetic or of biological origin.

[0012] Method for determining prognosis of breast cancer The present invention provides a method for determining the prognosis of breast cancer using all of the following 27 gene groups as indicators: ASPM (SEQ ID NO: 1), BCL11A (SEQ ID NO: 2), BF930764 (SEQ ID NO: 3), BIRC5 (SEQ ID NO: 4), and CYP2000 (SEQ ID NO: 5). Sequence number 4), C10orf3 (SEQ ID NO: 5), CCNB2 (SEQ ID NO: 6), CDC45L (SEQ ID NO: 7), CDCA8 (SEQ ID NO: 8), CENPF (SEQ ID NO: 9), FLJ10719 (SEQ ID NO: 10), FLJ11280 (SEQ ID NO: 11), FLJ14399 (SEQ ID NO: 12), HIS1 (SEQ ID NO: 13), HSPC150 (SEQ ID NO: 1 4), KIF2C (SEQ ID NO: 15), LOC51161 (SEQ ID NO: 16), MGC7036 (SEQ ID NO: 17), MKNK2 (SEQ ID NO: 18), PKMYT1 (SEQ ID NO: 19), PLK (SEQ ID NO: 20), PRC1 (SEQ ID NO: 21), PTP4A2 (SEQ ID NO: 22), RPS27L (SEQ ID NO: 23), STMN1 (SEQ ID NO: 24) , SULF2 (sequence number 25), TGS (sequence number 26), UBE2C (sequence number 27).

[0013] In a typical embodiment of the present invention, the expression level of each of the above genes in a sample collected from a breast cancer patient is measured. Such a biological sample is not particularly limited as long as it contains nucleic acid derived from tumor cells of a breast cancer patient, and examples thereof include breast cancer tumor tissue and breast cancer tumor cells collected from the breast cancer patient. Examples of the biological sample include breast tissue, nipple secretions, blood, serum, plasma, and the like collected from the breast cancer patient. The method of extracting DNA from these samples is also not particularly limited, and can be performed using or based on a known method. The expression level of each gene can be obtained by measuring the mRNA, protein, and the like of each gene, and can be obtained, for example, by measuring mRNA as in the examples described below. The method of measuring the amount of mRNA is not particularly limited, and for example, RT-quantitative PCR, RNA-Seq, and the like can be used. The method of measuring protein is not particularly limited, and for example, ELISA, protein array, and the like can be used. The expression level can also be measured using a reporter gene assay.

[0014] In the present invention, the genes used as prognostic indicators include known genes to which Genbank Accession No. is assigned as shown below. More specifically, the base sequences of the cDNA sequences of the genes include those shown in SEQ ID NOs: 1 to 27. The name of each gene, and the correspondence between the Genbank Accession No. and the SEQ ID NO in a typical embodiment are shown in the table below.

[0015] [Table 1]

[0016] In a preferred embodiment, according to the method of the present invention, the 27 genes are divided into two groups, and the TP53 signature score, which is represented by the following formula, is calculated from the expression level of each gene. When the score is less than the cutoff value, the prognosis is judged to be good: TP53 signature score = [sum of logarithms of expression values ​​of gene group A below] / [sum of logarithms of expression values ​​of gene group B below] Genogroup A: ASPM, BCL11A, BF930764, BIRC5, C10orf3, CCNB2, CDC45L, CDCA8, CENPF, FLJ10719, HSPC150, KIF2C, PKMYT1, PLK, PRC1, STMN1, TGS, UBE2C Genogroup B: FLJ11280, FLJ14399, HIS1, LOC51161, MGC7036, MKNK2, PTP4A2, RPS27L, SULF2.

[0017] In such an embodiment, the logarithm of the expression value may be, for example, log n [Expression value] can be increased. Here, n is a preset value, and can be set, for example, within the range of 8≦n≦12, preferably within the range of 9≦n≦11, and typically n=10. In this embodiment, the cutoff value T can be set, for example, in the range of 1.50≦T≦1.80, preferably in the range of 1.60≦T≦1.70, and more preferably in the range of 1.65≦T≦1.70. In a typical embodiment of the present invention, the cutoff value T can be set to 1.67.

[0018] In a preferred embodiment of the present invention, the prognosis can be determined to be poor when the TP53 signature score is equal to or greater than the cutoff value.

[0019] According to the present invention, the prognosis of breast cancer patients can be predicted using a method based on the extremely simplified calculation formula as described above, and therefore using a very convenient method. In a typical embodiment of the present invention, the target breast cancer patients include Stage I-II breast cancer patients. Furthermore, according to the method of the present invention, it is possible to predict the prognosis of ER-positive patients and Stage I patients, whose prognosis was difficult to predict using conventional methods. group, lymph node metastasis negative patient group, Grade 1 patient group, patient group aged over 50 years, Ki-67 ≥ 10% This makes it possible to accurately predict the prognosis for this patient group.

[0020] An ER (estrogen receptor) positive patient is one in which staining is observed in breast cancer tissue by immunohistochemistry using an anti-ER antibody. ER positive patients have a better prognosis than ER negative patients, and there are relatively few cases of recurrence. However, as recurrence occurs at a certain frequency, a method to predict the prognosis of such patients has been eagerly awaited. A Stage I patient is one who is judged to be at Stage I according to the criteria set forth in the Clinical and Pathological Guidelines for Breast Cancer (Japan Breast Cancer Society) or the TNM classification of the Union for International Cancer Control (UICC). Stage I patients also have a relatively low risk of recurrence, but Recurrence is observed at a certain frequency. However, it is difficult to predict the prognosis in patients with a low risk of recurrence, and a method to predict the prognosis for these patients has been desired. Patients with negative lymph node metastasis are those in whom swelling of the lymph nodes surrounding breast cancer is not found by imaging diagnostics such as CT, MRI, and ultrasound. This refers to patients who are not considered to have lymph node metastasis or patients who have not been found to have lymph node metastasis in the pathological diagnosis of the regional lymph nodes. Although the risk of recurrence is relatively low in patients with negative lymph node metastasis, recurrence occurs at a certain frequency, so a method to predict the prognosis of such patients was desired. Grade 1 patients are those who are not considered to have lymph node metastasis according to the Nottingham histological grading classification, the classification by Black et al., the classification by Le Doussal et al., and and patients who are judged to be Grade 1 according to the criteria described in the nuclear grade classification in the "Guidelines for the Treatment of Breast Cancer." Grade 1 patients also have a relatively low risk of recurrence, but recurrence is observed at a certain frequency. However, it is difficult to predict the prognosis in patients with a low risk of recurrence, and a method for predicting the prognosis of these patients has been desired. Patients over 50 years of age also have a good prognosis compared to patients under 50 years of age, and there are relatively few cases of recurrence. However, recurrence is observed at a certain frequency, and therefore a method for predicting the prognosis of these patients has been desired. Group, Ki-67 ≥ 10% refers to the number of positive cells relative to the number of cells evaluated by immunohistochemistry using an antibody against the KI67 (MKI67) protein in breast cancer tissue. Patients with Ki-67 ≥ 10% were relatively more likely to relapse. Since these patients are at high risk, a method for predicting the prognosis of these patients has been desired. The method of the present invention answers this need and is extremely useful. Therefore, the present invention provides a method for determining the prognosis of breast cancer in ER-positive patients, a method for predicting the prognosis of breast cancer in Stage I breast cancer patients, and a method for predicting the prognosis of breast cancer in Stage I breast cancer patients. The present invention provides a method for determining the prognosis of breast cancer in patients with lymph node metastasis negative, a method for determining the prognosis of breast cancer in patients with Grade 1 breast cancer, a method for determining the prognosis of breast cancer in patients over 50 years of age, and a method for determining the prognosis of breast cancer in patients with Ki-67≧10%. The method of the present invention is applicable to ER-positive patient groups, Stage I patient groups, lymph node metastasis-negative patient groups, and Grade 1 The present invention can diagnose the prognosis with high accuracy in patient groups with a relatively low risk of recurrence, such as a patient group ...

[0021] Furthermore, as described above, according to the present invention, the prognosis of breast cancer patients can be predicted. Generally, in cancer, those with high cell proliferation capacity (cells that multiply easily) tend to have a poor prognosis. Therefore, it is expected that the cancer cells of patients who are judged to have a poor prognosis by the method of the present invention have a relatively high cell proliferation capacity. On the other hand, anticancer drugs generally inhibit the DNA damage that occurs during cell proliferation. They induce cell death by inhibiting the replication of proteins and suppressing proteins that affect cell division. Therefore, cells with high cell proliferation ability have the property that anticancer drugs are more effective against them. Therefore, according to the method of the present invention, it is also possible to determine the sensitivity of breast cancer patients to treatment with anticancer drugs that have an action site related to cell proliferation or cell division. A specific method can be performed according to the method for predicting the prognosis of breast cancer patients, and for example, when the TP53 signature score is equal to or greater than the cutoff value, the breast cancer patient can be determined to be sensitive to the anticancer drug treatment. Also, for example, when the TP53 signature score is less than the cutoff value, the breast cancer patient can be determined to be less sensitive to the anticancer drug treatment. When the TP53 signature score is equal to or higher than the cutoff value, the disease progresses rapidly and the prognosis is generally poor. However, when chemotherapy is administered, particularly with anticancer drugs that act on cell proliferation or cell division, the rate of pathological complete response (pCR) is expected to be significantly higher than when the score is below the cutoff value. Therefore, when the TP53 signature score is equal to or higher than the cutoff value, the rate of pCR is expected to be significantly higher. On the other hand, if the TP53 signature score is below the cutoff value, a treatment strategy of early surgery without preoperative chemotherapy can be selected.

[0022] In the present invention, examples of breast cancer treatment include surgical therapy, drug therapy, and radiation therapy. In the present invention, drug therapy for breast cancer is not particularly limited, and includes hormone therapy, chemotherapy, therapy using molecular targeted drugs, and combinations thereof. Drugs used in hormone therapy are not particularly limited, and include, for example, antiestrogens such as tamoxifen, toremifene, fulvestrant, and raloxifene, and aromatase inhibitors such as anastrozole, letrozole, and exemestane. Drugs used in chemotherapy are not particularly limited, and include, for example, doxorubicin, cyclophosphamide, paclitaxel, and docetaxel. Molecular targeted drugs are also not particularly limited, and include, for example, anti-HER2 agents such as trastuzumab, pertuzumab, and lapatinib, CDK4 / 6 inhibitors such as palbociclib and abemaciclib, PARP inhibitors such as olaparib, and immune checkpoint inhibitors such as atezolizumab.

[0023] In the present invention, "a method for determining the prognosis of breast cancer using all 27 types of gene groups as indicators" includes not only methods that use only the above 27 genes as indicators, but also methods that use a gene group consisting of the 27 genes plus one or several genes (e.g., 1 gene, 2 genes, 3 genes, 4 genes) as indicators, within the scope of obtaining the effects of the present invention (within the scope of the unchanged conclusion).

[0024] According to the present invention, by using the above 27 genes as indicators, ANAPC7 (SEQ ID NO: 28), PTTG1 (SEQ ID NO: 29), CENPE (SEQ ID NO: 30), MUTYH (SEQ ID NO: 31), MGC45866 ( SEQ ID NO: 32), MAPREL1 (SEQ ID NO: 33), TMSNB (SEQ ID NO: 34), TTC12 (SEQ ID NO: 35), HCAP-G (SEQ ID NO: 36), CEAL1 (SEQ ID NO: 37), FLJ33962 (SEQ ID NO: 38), GMNN (SEQ ID NO: 39), ENST00000332343 (SEQ ID NO: 40), HEC (SEQ ID NO: 41), GMPR2 (SEQ ID NO: 42), TncRNA (SEQ ID NO: 43), SMOC2 (SEQ ID NO: 44), DNAJC9 (SEQ ID NO: No. 45), RAD54B (SEQ ID NO: 46), CKS2 (SEQ ID NO: 47), I_960269 (SEQ ID NO: 48), BAG1 (SEQ ID NO: 49), AL137566 (SEQ ID NO: 50), BRRN1 (SEQ ID NO: 51), CDC2 ( SEQ ID NO: 52), ZF (SEQ ID NO: 53), THC1577090 (SEQ ID NO: 54), CDKN2C (SEQ ID NO: 55), I_1842252 (SEQ ID NO: 56), SDOS (SEQ ID NO: 57), SNAPC2 (SEQ ID NO: 58) , EVI2A (SEQ ID NO: 59), V4b (SEQ ID NO: 60), BC007934 (SEQ ID NO: 61), ECT2 (SEQ ID NO: 62), RAD21 (SEQ ID NO: 63), MCM7 (SEQ ID NO: 64), AK097469 (SEQ ID NO: 6 5), MGC39900 (SEQ ID NO: 66), FLJ21439 (SEQ ID NO: 67), STATIP1 (SEQ ID NO: 68 ), DKFZp434L142 (SEQ ID NO: 69), and PLAT (SEQ ID NO: 70) can be used as indicators to determine the prognosis of breast cancer.

[0025] Breast cancer prognosis kit The present invention relates to a nucleic acid comprising at least a part of the base sequence of any of the genes in Table 1. The kit of the present invention can be appropriately configured depending on the method, means, etc. for measuring the expression level of each of the above-mentioned genes. For example, the length of a nucleic acid consisting of at least a part of the base sequence of any of the genes in Table 1 included in the kit of the present invention can be several tens of base pairs, and the specific part (base sequence) can be easily obtained from various databases such as the database in Table 1 (Genbank). Those skilled in the art can prepare them appropriately based on the above. They can also be used in the form of a DNA chip or a probe for Northern blotting, a primer for PCR, etc., depending on the method for measuring the expression level of each gene. Furthermore, if necessary, the polynucleotide or oligonucleotide may be labeled with an appropriate labeling substance such as a radioactive substance, a fluorescent substance, or a dye.

[0026] The above kit may optionally contain other elements or components, such as various reagents, enzymes, buffer solutions, reaction plates (containers), and the like. EXAMPLES

[0027] Prognostic prediction of TP53 signature in 216 cases of stage I-II breast cancer Formalin-fixed paracetamol of surgically resected breast cancer tumor tissue from 216 patients with stage I-II breast cancer Total RNA was extracted from FFPE tissue and analyzed using nCounter (NanoString). We performed gene expression analysis.

[0028] The expression values ​​of 27 genes constituting the TP53 signature (gene group A: ASPM, BCL11A, BF930764, BIRC5, C10orf3, CCNB2, CDC45L, CDCA8, CENPF, FLJ10719, HSPC150, KIF2C, PKMYT1, PLK, PRC1, STMN1, TGS, UBE2C, gene group B: FLJ11280, FLJ14399, HIS1, LOC51161, MGC7036, MKNK2, PTP4A2, RPS27L, SULF2) were measured, and the TP53 signature score was calculated from the log10 value of the obtained expression value. That is, in this embodiment, the TP53 signature score was calculated as follows: TP53 signature score = [log10 sum of expression values ​​of gene group A below] / [log10 sum of expression values ​​of gene group B below]

[0029] Among the 216 cases, those with a TP53 signature score of 1.67 or higher were classified as TP53 signature mutant type, and those with a TP53 signature score of less than 1.67 were classified as TP53 signature wild type.

[0030] As a result, 99 cases were TP53 signature mutation type and 117 cases were wild type. A graph summarizing the recurrence-free survival rate and survival time of the TP53 signature wild type group (wt group) and the TP53 signature mutation type (mt group) is shown above. As shown in the upper left of Figure 1, the TP53 signature mutation type had a significantly poorer prognosis (recurrence-free survival time) than the wild type (P = 0.0044). In this embodiment, the above method using TP53 signature as an index may be simply referred to as the "method of the embodiment".

[0031] Univariate analysis of recurrence-free survival time was performed using the Cox proportional hazards model for clinical pathological patient background factors and TP53 signature for breast cancer. Stage, presence or absence of lymph node metastasis, and TP53 signature were significantly associated with recurrence-free survival time. Ki-67 was significantly associated with no recurrence. A multivariate analysis was performed using the Cox proportional hazards model with only the factors that were found to be associated with recurrence-free survival in the univariate analysis. The TP53 signature was significantly associated with recurrence-free survival (hazard ratio 3.44, P=0.047). These results demonstrated that the TP53 signature is a predictor of recurrence-free survival.

[0032] [Table 2]

[0033] Next, the above 216 cases were diagnosed using the method described in Non-Patent Document 1. The gene groups whose expression levels were increased in patients with TP53 gene mutations (gene group C: ANAPC, ASPM, BCL11A, BF930764, BIRC5, C10orf3, CCNB2, CDC45L, CDCA8, CENPE, CENPF, FLJ10719, HSPC150, KIF2C, MAPR1, MGC45866, MUTYH, PKMYT1, PLK, PRC1, STMN1, TGS, UBE2C; gene group D: FLJ11280, FLJ14399, HIS1, LOC51161, MGC7036, MKNK2, PTP4A2, RPS27L, SULF2) were identified. The ratio of the sum of the expression levels of the genes with decreased expression ([sum of expression values ​​of gene group C] / [sum of expression values ​​of gene group D]) was calculated. In the present specification, [sum of expression values ​​of gene group C] / [sum of expression values ​​of gene group D] is referred to as TP53 signature'. In this method, when the ratio of the sum of the expression levels of gene group C and gene group D is equal to or greater than the cutoff value of 0.78, the patient was diagnosed as TP53 signature' mutant type, and when it is less than 0.78, the patient was diagnosed as TP53 signature' wild type. In this embodiment, the above method using TP53 signature' as an index may be simply referred to as "Oncotarget diagnostic method". As shown in the upper right of FIG. 1, the TP53 signature' mutant type also had a significantly poorer prognosis (recurrence-free survival period) than the wild type, but as described above, the predictability of prognosis was poorer than that of the method of the embodiment using TP53 signature (FIG. 1, upper left, P=0.0044). Ta.

[0034] Next, we compared the recurrence-free survival time between the TP53 signature mutation group and the wild type group for 148 ER-positive cases out of 216 cases of stage I-II breast cancer. There were 53 TP53 signature mutation and 108 wild type cases, respectively. The results are shown in the lower left of Figure 1. The TP53 signature mutation was significantly longer than the wild type. The prognosis (recurrence-free survival) was significantly worse in the 148 patients with ER-positive tumors (P = 0.0239). As a result of performing the Oncotarget diagnostic method on these cases, there was no significant difference in prognosis (recurrence-free survival) between the TP53 signature' mutation type and the wild type (Figure 1, bottom right, P = 0.073).

[0035] Of 216 cases of stage I-II breast cancer, 115 cases of stage I were classified as TP53 signature mutation group. The recurrence-free survival period was compared between the wild type and TP53 signature mutation groups. The results are shown in the upper left of Figure 2. There were 48 cases of TP53 signature mutation and 67 cases of wild type, respectively. The prognosis (recurrence-free survival period) of the TP53 signature mutation type was significantly worse than that of the wild type (P = 0.0239). In contrast, the results of the Oncotarget diagnostic method for 115 Stage I cases showed no significant difference in prognosis (recurrence-free survival period) between the TP53 signature mutation type and the wild type (Figure 2, upper right, P = 0.44).

[0036] Of 216 cases of stage I-II breast cancer, 154 cases with negative lymph node metastasis were targeted to compare the recurrence-free survival time between the TP53 signature mutation group and the wild type group. There were 66 TP53 signature mutation and 88 wild type cases, respectively. The results are shown in the lower left of Figure 2. The TP53 signature mutation type had a significantly poorer prognosis (recurrence-free survival time) than the wild type (P = 0.018). In contrast, the lymph node metastasis The Oncotarget diagnostic method was performed on 154 cases with negative metastasis, and the results showed that TP53 signature mutations were There was no significant difference in prognosis (recurrence-free survival) between the wild-type and control (Figure 2, bottom right, P=0.17).

[0037] Among 216 patients with stage I-II breast cancer, 55 patients with grade 1 were selected as the TP53 signature mutation group. The recurrence-free survival time was compared between the TP53 signature mutation type and wild type group. There were 11 cases of TP53 signature mutation type and 44 cases of wild type, respectively. The results are shown in Figure 3, upper left. The TP53 signature mutation type had a significantly poorer prognosis (recurrence-free survival time) than the wild type (P = 0.0002). In contrast, when the Oncotarget diagnostic method was performed on 55 grade 1 cases, there was no significant difference in prognosis (recurrence-free survival time) between the TP53 signature mutation type and the wild type (Figure 3, upper right, P = 0.091).

[0038] Of 216 cases of stage I-II breast cancer, 149 cases aged 51 years or older were included in the study, and the recurrence-free survival time was compared between the TP53 signature mutation group and the wild type group. There were 74 and 75 TP53 signature mutation and wild type cases, respectively. The results are shown in the lower left of Figure 3. The TP53 signature mutation type had a significantly worse prognosis (recurrence-free survival time) than the wild type (P = 0.018).

[0039] Among 216 patients with stage I-II breast cancer, 152 patients with Ki-67 of 10% or higher were screened for TP53 signature mutations. The recurrence-free survival time was compared between the variant group and the wild-type group. There were 92 cases of TP53 signature mutation type and 60 cases of wild-type, respectively. The results are shown in Figure 4, left. The prognosis (recurrence-free survival time) of the TP53 signature mutation type was significantly worse than that of the wild-type (P = 0.017). In contrast, the Oncotarget diagnostic method was performed on 152 cases with Ki-67 of 10% or more, and the results showed that the prognosis was significantly worse between the TP53 signature mutation type and the wild-type. There was no significant difference in prognosis (recurrence-free survival) between the types (Figure 4, right, P = 0.059).

Claims

1. A method for determining the prognosis of breast cancer using all of the following 27 gene groups as indicators: ASPM (SEQ ID NO: 1) BCL11A (SEQ ID NO:2) BF930764 (SEQ ID NO: 3) BIRC5 (SEQ ID NO:4) C10orf3 (SEQ ID NO:5) CCNB2 (SEQ ID NO:6) CDC45L (SEQ ID NO: 7) CDCA8 (SEQ ID NO:8) CENPF (SEQ ID NO:9) FLJ10719 (SEQ ID NO: 10) FLJ11280 (SEQ ID NO: 11) FLJ14399 (SEQ ID NO: 12) HIS1 (SEQ ID NO: 13) HSPC150 (SEQ ID NO: 14) KIF2C (SEQ ID NO: 15) LOC51161 (SEQ ID NO: 16) MGC7036 (SEQ ID NO: 17) MKNK2 (SEQ ID NO: 18) PKMYT1 (SEQ ID NO: 19) PLK (SEQ ID NO:20) PRC1 (SEQ ID NO:21) PTP4A2 (SEQ ID NO:22) RPS27L (SEQ ID NO:23) STMN1 (SEQ ID NO:24) SULF2 (SEQ ID NO:25) TGS (SEQ ID NO:26) UBE2C (SEQ ID NO:27)

2. The method according to claim 1, wherein the prognosis is judged to be good when the TP53 signature score calculated by the following formula is less than a cutoff value: TP53 signature score = [sum of logarithms of expression values ​​of gene group A below] / [sum of logarithms of expression values ​​of gene group B below] Gene group A ASPM (SEQ ID NO: 1) BCL11A (SEQ ID NO:2) BF930764 (SEQ ID NO: 3) BIRC5 (SEQ ID NO:4) C10orf3 (SEQ ID NO:5) CCNB2 (SEQ ID NO:6) CDC45L (SEQ ID NO: 7) CDCA8 (SEQ ID NO:8) CENPF (SEQ ID NO:9) FLJ10719 (SEQ ID NO: 10) HSPC150 (SEQ ID NO: 14) KIF2C (SEQ ID NO: 15) PKMYT1 (SEQ ID NO: 19) PLK (SEQ ID NO:20) PRC1 (SEQ ID NO:21) STMN1 (SEQ ID NO:24) TGS (SEQ ID NO:26) UBE2C (SEQ ID NO:27) Gene group B FLJ11280 (SEQ ID NO: 11) FLJ14399 (SEQ ID NO: 12) HIS1 (SEQ ID NO: 13) LOC51161 (SEQ ID NO: 16) MGC7036 (SEQ ID NO: 17) MKNK2 (SEQ ID NO: 18) PTP4A2 (SEQ ID NO:22) RPS27L (SEQ ID NO:23) SULF2 (SEQ ID NO:25)

3. The method according to claim 1 or 2, wherein the group of genes is derived from a sample prepared from breast cancer tissue or breast cancer cells taken from a breast cancer patient.

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