KR20210127519A - Methods for predicting risk of recurrence of advanced breast cancer patients - Google Patents

Abstract

The present invention relates to a method for predicting prognosis of a progressive breast cancer patient. In detail, the present invention relates to a method for predicting prognosis of a progressive breast cancer by combination of a propagation-related gene and an immune-related gene. Through the method of the present invention, there is an effect of more accurately predicting prognosis with respect to future metastasis, recurrence or metastasis recurrence, and disease-free/non-remote metastasis survival rate of the progressive breast cancer patient, thereby being usefully used for presenting clues for direction of future breast cancer treatment.

Description

Methods for predicting risk of recurrence of advanced breast cancer patients

The present invention relates to a method for predicting the prognosis of a patient with advanced breast cancer, and more particularly, to a method for predicting the prognosis of an advanced breast cancer by a combination of a proliferation-related gene and an immune-related gene.

Breast cancer is the most common cancer among women and the second leading cause of death. In 2001, the prevalence of breast cancer was 90-100 per 100,000 in the United States and 50-70 per 100,000 in Europe. The incidence of this disease is increasing worldwide. Risk factors for breast cancer include race, age, and mutations in the cancer suppressor genes BRCA-1 and BRCA-2 and p53. Alcohol intake, high-fat diet, lack of exercise, exogenous postmenopausal hormones and ionizing radiation also increase the risk of developing breast cancer. Estrogen receptor and progesterone receptor negative breast cancer (“ER-” and “PR-”, respectively), large tumor size, high-grade cytology results, and poor prognosis in patients younger than 35 years of age (Goldhirsch et al. (2001). J (Clin. Oncol. 19: 3817-27). It is estimated that approximately 212,000 new cases of invasive breast cancer and 58,000 new cases of non-invasive breast cancer will be diagnosed in 2005, and 40,000 women are expected to die from breast cancer.

Current treatment methods for breast cancer often require additional adjuvant treatment to reduce future recurrence, such as post-surgery, chemotherapy, anti-hormonal therapy, targeted therapy, or radiation therapy. The characteristics are different, therefore, the basis for further treatment is being established by considering the presence or absence of hormone receptor (ER and PR) expression, overexpression, metastasis status, and lymph node metastasis (positive or negative) through biopsy. Although such clinical information is widely used as an indicator to determine treatment, the heterogeneity of cancer is greater than the phenotype of the clinical indicator, so the prognosis of all cancers cannot be judged, and its usefulness is also exposed.

On the other hand, breast cancer is generally classified into several main stages according to the degree of invasion and metastasis: early stage, locally advanced stage, locally persistent stage and metastatic stage. Early breast cancers include non-invasive breast cancers such as lobular carcinoma in situ (“LCIS”) and ductile carcinoma in situ (“DCIS”). In general, breast cancer is staged according to the Tumor Node Metastasis (“TNM”) proposed by the American Joint Committee on Cancer (AJCC Cancer Staging Manual, 6th Edition). The TNM classification system defines breast cancer as seven separate stages: 0, I, IIA, IIB, IIIA, IIIB and IV. The subtypes of stage 0, I and stage II are usually early stage breast cancer. Some subtypes of stage II and stage III are advanced breast cancer. Stage IV generally refers to metastatic breast cancer.

For early breast cancer, the 5-year survival prognosis is generally greater than 90%, but for advanced breast cancer, this number drops to 70-80%. In the case of metastatic breast cancer, the 5-year survival rate is generally only around 20%. The most common distant metastases in breast cancer include the lungs, liver, bones, lymph nodes, skin, and central nervous system (brain). Once diagnosed with metastatic breast cancer, patients are expected to live an average of 18 to 24 months. Treatment of metastatic breast cancer is almost impossible, and the treatment for this disease is essentially pain relief.

Therefore, although various products for diagnosing the prognosis of breast cancer for predicting the prognosis of breast cancer have been developed, most of the products are focused on predicting the prognosis of early breast cancer (pN0/1). However, in the case of advanced breast cancer, most patients are receiving chemotherapy because the recurrence rate is higher than that of early breast cancer, but among them, there are some patients with good prognosis. can give

Accordingly, the present inventors have conducted intensive research to develop a method for accurately predicting the prognosis of patients with advanced breast cancer, and as a result, among various methods used to predict the prognosis of patients with advanced breast cancer, the prognosis predicting ability of patients with advanced breast cancer is particularly excellent. After confirming the method, the present invention was completed by optimizing it to be suitable for predicting the prognosis of patients with advanced breast cancer.

Accordingly, an object of the present invention is to provide a method for predicting prognosis of breast cancer including the following steps in order to provide information necessary for predicting the prognosis of patients with pN2 or pN3 advanced breast cancer:

(a) obtaining a biological sample from a breast cancer patient;

(b) UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) from the sample of step (a) And measuring the mRNA expression level of BTN3A2 (Butyrophilin subfamily 3 member A2);

(c) normalizing the mRNA expression level of the gene;

(d) evaluating the size of the tumor of the breast cancer patient;

(e) calculating the numerical value by substituting the standardized value in step (c) and the size of the tumor in step (d) into the following equations 1 and 2

[Equation 1]

Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h

[Equation 2]

BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0

BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤ 10

BCT score 10 if 0.8*U-BS - 13.71>10

(The value of the prognostic gene is a standardized mRNA expression value calculated using a standard gene, and the tumor size represents the long axis length of the tumor,

wherein a is 0.16 to 1.09, b is 0 to 0.71, c is 0 to 0.53, d is 0 to 0.57, e is 0 to 0.35, f is -1.02 to 0, g is 0.25 to 1.52, and h is 0.19 to 2.25 ); and

(f) When the value in step (e) is more than 7, the prognosis is predicted as a high-risk group with a poor prognosis, as a medium-risk group when the value is 4.5 or more and 7 or less, and when the value is less than 4.5, the prognosis is predicted as a low-risk group with a good prognosis step to do.

In order to achieve the above object, the present invention provides a breast cancer prognosis prediction method comprising the following steps in order to provide information necessary for predicting the prognosis of pN2 or pN3 advanced breast cancer patients:

(a) obtaining a biological sample from a breast cancer patient;

(b) UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) from the sample of step (a) And measuring the mRNA expression level of BTN3A2 (Butyrophilin subfamily 3 member A2);

(c) normalizing the mRNA expression level of the gene;

(d) evaluating the size of the tumor of the breast cancer patient;

(e) calculating the numerical value by substituting the standardized value in step (c) and the size of the tumor in step (d) into the following equations 1 and 2

[Equation 1]

Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h

[Equation 2]

BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0

BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤ 10

BCT score 10 if 0.8*U-BS - 13.71>10

(The value of the prognostic gene is a standardized mRNA expression value calculated using a standard gene, and the tumor size represents the long axis length of the tumor,

wherein a is 0.16 to 1.09, b is 0 to 0.71, c is 0 to 0.53, d is 0 to 0.57, e is 0 to 0.35, f is -1.02 to 0, g is 0.25 to 1.52, and h is 0.19 to 2.25 ); and

(f) When the value in step (e) is more than 7, the prognosis is predicted as a high-risk group with a poor prognosis, as a medium-risk group when the value is 4.5 or more and 7 or less, and when the value is less than 4.5, the prognosis is predicted as a low-risk group with a good prognosis step to do.

Hereinafter, the present invention will be described in detail.

The present invention provides a breast cancer prognosis predicting method comprising the following steps in order to provide information necessary for predicting the prognosis of pN2 or pN3 advanced breast cancer patients:

(a) obtaining a biological sample from a breast cancer patient;

(b) UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) from the sample of step (a) And measuring the mRNA expression level of BTN3A2 (Butyrophilin subfamily 3 member A2);

(c) normalizing the mRNA expression level of the gene;

(d) evaluating the size of the tumor of the breast cancer patient;

(e) calculating the numerical value by substituting the standardized value in step (c) and the size of the tumor in step (d) into the following equations 1 and 2

[Equation 1]

Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h

[Equation 2]

BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0

BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤ 10

BCT score 10 if 0.8*U-BS - 13.71>10

(The value of the prognostic gene is a standardized mRNA expression value calculated using a standard gene, and the tumor size represents the long axis length of the tumor,

wherein a is 0.16 to 1.09, b is 0 to 0.71, c is 0 to 0.53, d is 0 to 0.57, e is 0 to 0.35, f is -1.02 to 0, g is 0.25 to 1.52, and h is 0.19 to 2.25 ); and

(f) When the value in step (e) is more than 7, the prognosis is predicted as a high-risk group with a poor prognosis, as a medium-risk group when the value is 4.5 or more and 7 or less, and when the value is less than 4.5, the prognosis is predicted as a low-risk group with a good prognosis step to do.

In the present invention, the term "prognosis" refers to the progress of the disease during or after treatment for breast cancer, and preferably refers to the progress of the disease after treatment, but is not limited thereto. In addition, the term "progression of disease" is a concept including cure, recurrence, metastasis, metastatic recurrence, disease-free survival or distant metastasis survival rate of cancer, and preferably means disease-free survival rate or distant metastasis-free survival rate. It is not limited.

The "metastatic recurrence" in the present invention refers to local metastatic recurrence that has metastasized to the site of occurrence of breast cancer before treatment and/or to a site within the ipsilateral breast and/or contralateral breast, and distant metastatic recurrence such as lung, liver, bone, lymph node, skin, and brain. It is a concept that includes distant metastatic recurrence caused by metastasis to a site. Preferably, the metastatic recurrence in the present invention may be distant metastatic recurrence, but is not limited thereto.

In the present invention, the term "metastatic recurrence" means, after initial treatment, transformed, that is, cancer cells derived from at least one breast tumor that are separated from the tumor and continue to grow as cancer at a location away from the tumor (hereinafter referred to as "remote site") say to do The remote site may be, for example, within one or more lymph nodes, which may be mobile or stationary, may be ipsilateral or contralateral to the tumor, and may be above the clavicle or in the armpit or the like.

The prognosis of breast cancer is mainly based on the size of the tumor (T), the status of metastasis to the lymph node periphery (N), and the stage of the disease after surgery (TNM stage), which evaluates the distant metastasis (M) to other organs. ) is determined by The prognostic predictions of patients classified according to the TNM stage are different even at the same stage. That is, even in breast cancer of the same stage, prognosis prediction can be determined by whether estrogen or progesterone receptor (ER or PR) is expressed and whether or not HER2 (human epidermal growth factor receptor 2) is overexpressed or gene amplification. Even for breast cancer of the same stage, the condition and prognosis are significantly different depending on the expression of estrogen receptor, progesterone receptor, or HER2.

Therefore, recently, the characteristics of breast cancer are classified genetically and molecularly (Table 1), and the results and prognosis according to the treatment are also reported to be different depending on the subtype, so it is used as an index for the selection of surgical methods or chemotherapy.

Breast cancer in the present invention may preferably be estrogen receptor and/or progesterone receptor positive and HER2 negative breast cancer, and most preferably may be luminal type A breast cancer, but is not limited thereto.

On the other hand, in the case of breast cancer, the higher the stage, the more advanced the cancer, and the prognosis is also poor. Breast cancer is divided into stages 0-4. Breast cancer uses TNM staging, and three factors are required to determine TNM staging. There are stage T, which is determined by the size and characteristics of the cancer itself, stage N, which is determined by the degree of lymph node involvement, and stage M, which is determined by whether or not there is metastasis to sites other than the breast. The pathological characteristics in each stage are summarized in Table 2 below.

In the present invention, the breast cancer may be advanced breast cancer, more specifically, breast cancer corresponding to pN2 or pN3 stage, and most preferably breast cancer classified as stage 2 or stage 3 according to TNM stage.

Hereinafter, each step of the breast cancer prognosis prediction method in the present invention will be described in more detail.

(a) obtaining a biological sample from a patient with advanced breast cancer of pN2 or pN3;

In the present invention, the biological sample may be breast cancer tissue of a breast cancer patient. The breast cancer tissue may also contain some normal cells, and preferably, a formalin-fixed paraffin-embedded (FFPE) sample of breast cancer tissue containing cancer cells, fresh tissue, and frozen tissue. It may be selected from the group consisting of, but is not limited thereto.

In the present invention, the pN refers to a method of determining whether metastasis to a lymph node is metastasized by pathological classification among methods for classifying the stages of breast cancer. The pathological classification method, also called postsurgical histopathological classification, is a method of classifying pathological stages by collecting information obtained from surgery or pathological examinations together with information obtained before starting treatment in breast cancer patients. way.

Among the pathological classification methods, pN is a classification method based on the degree of metastasis to the lymph nodes. In the present invention, the pN stage of the breast cancer patient may be pN2 stage or pN3 stage advanced breast cancer.

(b) UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) from the sample of step (a) And measuring the mRNA expression level of BTN3A2 (Butyrophilin subfamily 3 member A2);

Functioning as a prognostic marker for breast cancer in the present invention is UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1) and MKI67 (Marker of proliferation Ki-67) ), a proliferation-related gene group and BTN3A2 (Butyrophilin subfamily 3 member A2) immune-related genes.

Each of the genes may be a sequence of each gene known in the art or a sequence of a synonym for each gene, preferably a sequence of each gene derived from human, more preferably UBE2C (Gene ID: 11065) , TOP2A (Gene ID: 7153), RRM2 (Gene ID: 6241), FOXM1 (Gene ID: 2305), MKI67 (Gene ID: 4288), BTN3A2 (Gene ID: 11118), but is not limited thereto.

Synonyms and their sequences for each gene can be searched in GenBank.

For the method of measuring the mRNA expression level in the present invention, any method performed to measure the expression level of a gene in the art may be used, preferably microarray, polymerase chain reaction (PCR), RT-PCR, quantitative It may be selected from the group consisting of RT-PCR (qRT-PCR), real-time PCR, northern blot, DNA chip, and RNA chip, but is not limited thereto.

The measurement of the expression level of the target gene of the present invention is preferably detection of the expression level of the target gene, more preferably quantitative detection of the expression level of the target gene. In order to detect the expression level, it may be necessary to isolate mRNA from the sample tissue and to synthesize cDNA from mRNA. For the isolation of mRNA, an RNA isolation method from a sample known in the art may be used, and preferably, the sample is an FFPE sample, so it may be an mRNA isolation method suitable for the FFPE sample. For the cDNA synthesis process, a cDNA synthesis method known in the art may be used using mRNA as a template. Preferably, since the measurement of the expression level of the breast cancer prognostic marker of the present invention is quantitative detection of mRNA expression in the FFPE sample, it is measured by the mRNA isolation method and real time reverse transcription quantitative polymerase chain reaction (RT-qPCR) method for the FFPE sample. can be

In addition, in the present invention, the measurement of the expression level of the target gene may be performed according to a method known in the art, but an optical quantitative analysis system using a probe labeled with a reporter fluorescent dye and/or quencher fluorescent dye can be measured by The measurement is performed on commercially available equipment, eg ABIPRISM 7700?? Sequence Detection System??, Roche Molecular Biochemicals Lightcycler and software attached thereto may be used. Such measured data can be expressed as a measured value or a threshold cycle (Ct or Cp). The point at which the measured fluorescence value is first recorded as statistically significant is the threshold cycle, which appears in inverse proportion to the initial value at which the detection target exists as a template for the PCR reaction. It indicates that there are many detection targets.

(c) normalizing the mRNA expression level measured in step (b);

The expression level of the gene to be detected in the present invention needs to be standardized because there may be differences in the overall gene expression level or expression level depending on the target patient or sample. Normalization is achieved through a difference from the expression level or expression level of a gene that may indicate a difference in the basic expression level or expression level, preferably CTBP1 (C-terminal-binding protein 1), CUL1 (cullin 1) and UBQLN1 (Ubiquilin-1) can be performed by measuring the expression levels of one to three genes (or the average of the expression levels when a plurality of genes are selected) and calculating a ratio thereof.

(d) evaluating the size of the tumor of the breast cancer patient;

In the present invention, the size of the tumor refers to the length of the long axis of the cancer, and preferably refers to the length of the long axis of the cancer measured by the judgment of a pathologist. The size of the tumor is expressed in centimeters.

(e) calculating the numerical value by substituting the standardized value in step (c) and the size of the tumor in step (d) into Equation 1 below

[Equation 1]

Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h

(The value of the prognostic gene is a standardized mRNA expression value calculated using a standard gene, and the tumor size represents the long axis length of the tumor,

wherein a is 0.16 to 1.09, b is 0 to 0.71, c is 0 to 0.53, d is 0 to 0.57, e is 0 to 0.35, f is -1.02 to 0, g is 0.25 to 1.52, and h is 0.19 to 2.25 )

The prognostic score is calculated through a linear combination with the coefficient corresponding to each of the gene and tumor size. Proliferative genes and tumor size have positive coefficients, and immune genes have negative coefficients. Each coefficient is applied within the 95% confidence interval of the coefficient value (point estimate value) calculated as a result of the survival analysis, and the point estimate value of each coefficient is preferably used.

Preferably, the method for predicting the prognosis according to the present invention is related to the immune response and cell proliferation, which are two main biological characteristics that govern clinical outcomes of breast cancer patients, and the expression is stable in the FFPE specimen tissue, and the expression difference according to the prognosis Selects a large gene, calculates a coefficient for the genes and clinical information (tumor size) important for prognosis through Cox analysis, and multiplies the standardized gene expression value and tumor size It can be characterized in that the prognosis of breast cancer can be predicted by obtaining the BCT score according to Equation 1-1.

[Equation 1]

Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h

The degree to which prognostic predictors (genes, clinical information) affect the survival rate can be expressed as quantitative values through Cox proportional hazard analysis. The Cox proportional hazards model expresses the degree of influence of prognostic factors on survival rate through the relative hazard ratio (HR) value, which is a proportional value between the risk in the absence of a prognostic factor and the risk in the presence of a risk factor. If the value of the proportional hazard ratio (HR) is greater than 1, the risk increases compared to the absence of prognostic factors. The value obtained by converting the proportional hazard ratio for each prognostic factor into a log scale is called a coefficient for each factor, and this value is used as the coefficient of calculation of the BCT score model (Cox, David R. "Regression models and life-tables." Journal of the Royal Statistical Society. Series B (Methodological) (1972): 187-220.). The coefficient of the gene was determined to verify the validity of the result of the calculation formula through cross validation.

In the above formula, the standardized expression level of each gene is substituted for each “ΔCt_prognostic predictive gene”. The standardization is made through a difference with the expression level or expression level of a gene that can represent a difference in the basic expression level or expression level, preferably CTBP1 (C-terminal-binding protein 1), CUL1 (cullin 1) and In UBQLN1 (Ubiquilin-1), the expression level of one to three genes (or the average of the expression levels when a plurality of genes are selected) is measured and a ratio thereof is calculated.

Specifically, the value obtained by subtracting the expression value of each prognostic gene from the average expression value of the standardization genes consisting of CTBP1 (C-terminal-binding protein 1), CUL1 (cullin 1), and UBQLN1 (Ubiquilin-1) and then adding 30 is a value of “βCt_gene for prognosis prediction”, and this value becomes a standardized value of each gene for prognosis prediction. That is, the standardized value of each prognosis prediction gene is calculated by substituting the following formula:

[ΔCt_ prognostic gene = ((Ct_CTBP1+Ct_CUL1+Ct_UBQLN1)/3) - Ct_ prognostic gene + 30]

(The "prognostic gene" is UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) to be standardized. and BTN3A2 (Butyrophilin subfamily 3 member A2).

The "Ct" refers to the number of cycles when a certain amount of the PCR amplification product is amplified. When the real-time RT-PCR method is employed, since the change in fluorescence intensity is generally at the noise level and equal to 0 until the number of amplification cycles 1 to 10, these are regarded as sample blanks of the amplification product 0, and their standard deviation SD , and the fluorescence value multiplied by 10 is used as a threshold value, and the number of PCR cycles that first exceeds the threshold value is used as a cycle threshold (Ct) value. Therefore, when there are many amplification products, the Ct value becomes a small value, and when there are few amplification products, the Ct value becomes a large value.

In the present invention, standard genes were used to standardize the expression values of each prognostic gene, and the average Ct value of three standard genes was used to minimize technical errors that may occur during the test.

In the present invention, in order to express the calculated value of [Equation 1] as an intuitive numerical value, it is converted into a value between 0 and 10 by a linear transformation such as [Equation 2] below.

[Equation 2]

BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0

BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤ 10

BCT score 10 if 0.8*U-BS - 13.71>10

(f) If the value in step (e) is more than 7, the prognosis is predicted as a high-risk group with a poor prognosis, as a medium-risk group when the value is 4.5 or more and 7 or less, and when the value is less than 4.5, the prognosis is predicted as a low-risk group with a good prognosis step to do

According to an embodiment of the present invention, the accuracy of risk group classification in the breast cancer prognosis prediction method of the present invention was set to a point where the value of the hazard ratio for recurrence of the low-risk group, the medium-risk group, and the high-risk group was maximum.

In the present invention, when the BCT score calculated according to Equation 2 (BCT score) is more than 7, the prognosis is a high-risk group with a poor prognosis; A good low-risk group can predict the prognosis.

In the present invention, the "high-risk group" means a high probability of cancer metastasis, recurrence, or metastatic recurrence if the patient maintains breast cancer therapy after treatment, a 5-year disease-free/metastatic survival rate, or a 10-year disease-free/metastatic recurrence rate This refers to a group of patients with a low survival rate, and the “moderate risk group” refers to a patient with a low probability of metastasis, recurrence, or metastatic recurrence if the ongoing breast cancer therapy is maintained, or a 5-year disease-free/metastatic-free survival rate or 10 years It means a group of patients with a high disease-free/metastatic survival rate, and the “low-risk group” means a patient with a low probability of metastasis, recurrence, or metastatic recurrence even if the patient maintains or discontinues ongoing breast cancer therapy, or has a 5-year disease-free/ It refers to a group of patients with a high metastasis-free survival rate or 10-year disease-free/metastatic survival rate.

More specifically, the patient classified as a "high-risk group" according to the method of the present invention may be a patient group in which it is difficult to expect a good prognosis with standard drug therapy alone. Therefore, it may be necessary to apply additional therapeutic regimens such as combination drug therapy.

Specifically, selective estrogen receptor modulator (tamoxifen, toremifene, etc.), selective estrogen receptor degrader (fulvestrant, etc.), anromatase inhibitor (anastrozole, letrozole, exemestane, etc.), GnRH analog (goserelin, leuprolide, etc.), progestins (megestrol acetate, etc.) , HER2 inhibitors (trastuzumab, emtasine, pertuzumab, lanitinib, etc.), angiogenesis inhibitors (bevacizumab, etc.), mTOR inhibitors (everolimus, etc.), cyclin-dependent kinase inhibitors (palbociclib, etc.), chemotherapeutic agents (decetaxel, paclitaxel, taxane, gemcitabine, etc.) cisplatin, etc.) and immunotherapy (pembrolizumab, nivolumab, etc.) for high-risk patients who were receiving only standard treatment regimens of one drug selected from the group consisting of one or more drugs selected from the above drug groups, or It may be desirable to use in combination with a novel anticancer agent or the like.

The patient classified into the "moderate risk group" according to the method of the present invention may be a patient group in which a good prognosis can be expected even with standard drug therapy for breast cancer.

A patient classified as a "low-risk group" according to the method of the present invention may be a patient group in which a good prognosis can be expected even if the drug is selectively excluded or discontinued during standard drug treatment.

In the present invention, the "standard drug treatment" means selective estrogen receptor modulator (tamoxifen, toremifene, etc.), selective estrogen receptor degrader (fulvestrant, etc.), anromatase inhibitor (anastrozole, letrozole, exemestane, etc.), GnRH analog (goserelin, leuprolide, etc.), progestins (megestrol acetate, etc.), HER2 inhibitors (trastuzumab, emtasine, pertuzumab, lanitinib, etc.), angiogenesis inhibitors (bevacizumab, etc.), mTOR inhibitors (everolimus, etc.), cyclin-dependent kinase inhibitors (palbociclib, etc.), chemotherapeutic agents (decetaxel, etc.) It refers to a treatment regimen in which only one drug selected from the group consisting of paclitaxel, taxane, gemcitabine, cisplatin, etc.) and immunotherapy (pembrolizumab, nivolumab, etc.) is administered to the patient.

In the present invention, the term “5 years” or “10 years” refers to 5 years or 10 years from the time the cancer was removed by surgery of a primary breast cancer patient (ie, from the date of surgery).

That is, the breast cancer prognosis prediction algorithm according to Equation 1 or Equations 1 and 2 of the present invention is a proliferation-related gene, immune-related gene and clinical information (cancer size and pN stage) closely related to the prognosis of breast cancer for a wide range of clinical samples. ), and its prognostic predictive power is very good in that it shows a higher prognostic power than other models such as the conventional clinical information-based prognostic evaluation model.

The method of the present invention has the effect of more accurately predicting the future metastasis, recurrence or metastatic recurrence of patients with advanced breast cancer, and the prognosis for disease-free/metastatic survival rate, suggesting a clue for the future direction of breast cancer treatment It can be useful for the purpose of

1 is a diagram showing the disease-free and distant metastasis-free survival rates of all patients (76 patients) analyzed in an embodiment of the present invention.
2 is a diagram showing disease-free and distant metastasis-free survival rates in each patient group after classifying advanced breast cancer patients into high-risk and low-risk groups based on the BCT score of 7 of the present invention.
3 is a diagram showing disease-free and distant metastasis-free survival rates in each patient group after classifying advanced breast cancer patients into high-risk, medium-risk and low-risk groups based on the BCT score of 7 and 4.5 of the present invention.

Hereinafter, the present invention will be described in detail.

However, the following examples are merely illustrative of the present invention, and the content of the present invention is not limited to the following examples.

1. Target patient group selection and sample collection

The present inventor discovers a gene set that can predict the prognosis of an early breast cancer patient through a prior study (Korean Patent No. 10-1896545), and uses a tissue sample containing the patient's cancer cells to determine the prognosis of an early breast cancer patient A predictive algorithm has been developed.

Therefore, in this study, whether the developed algorithm for predicting the prognosis of a patient with early breast cancer can accurately predict the prognosis of the patient even in the case of advanced breast cancer (ie, breast cancer with lymph node metastasis status of pN2 or pN3), if the prognosis of the patient can be predicted We wanted to check what is the threshold of the algorithm that can distinguish them.

First, HR+/HER2- breast cancer patients who can use primary cancer tissue and lymph node metastasis tissue were investigated, and finally, patients were selected and related clinical information was collected.

For the selected patients, a sample to be used for analysis was finally collected through analysis and sample preparation by a pathologist. To confirm that the sample was an accurate advanced breast cancer tissue, the following samples were excluded.

* If the invasive tumor cannot be secured by visiting our hospital after resection of the lesion in an external hospital

* When the size of the invasive tumor is small (less than 0.5 cm) according to microscopic observation

* In the case of ductal carcinoma in situ (DCIS) in order to prevent the interference effect by the intraductal carcinoma part

* If the patient's paraffin block sample cannot be found

* When looking at the pathologic nodal status, it is not pN2/3

The sample of the selected patient after the pathological examination of the sample by the pathologist was confirmed whether the sample was suitable for the algorithm according to the present invention by performing the following additional processing steps.

* When the sample slide of the selected patient is checked under a microscope and contains an invasive tumor and the size of the tumor is at least 3 mm ²

* If a sufficient amount of the patient's paraffin-embedded tumor block remains for examination

According to the above method, a total of 76 pN2/3 breast cancer patients who had undergone hormonal treatment and appropriate chemotherapy after surgery were selected.

The survival data of the selected 76 patients are shown in Table 3 and Figure 1 below:

As can be seen in Table 1 above, the 5-year and 10-year disease-free and distant metastasis-free survival rates of all patients were the same. This means that most of the first recurrent events during the postoperative follow-up period in this patient group were distant metastases. The 5-year disease-free/metastatic survival rate of the patient group used in this analysis was 76.3% (67.3%-86.5%), and the 10-year disease-free survival rate was 68.3% (58.6%-79.7%).

2. Evaluation of algorithms for predicting prognosis in patients with advanced breast cancer

After analyzing the expression level of the gene applied to the following formula in the sample of the advanced breast cancer patient (pN2 or pN3 patient) selected according to the above method, it was analyzed according to Equations 1 and 2:

[Equation 1]

Unscaled BCT score (U-BS) = 0.63*△Ct_UBE2C+0.32*△Ct_TOP2A + 0.13*△Ct_RRM2+0.02*△Ct_FOXM1+0.04*△Ct_MKI67-0.42*△Ct_BTN3A2+0.89*Tumor_size(cm)+1.22

(The value of the prognostic gene is the standardized mRNA expression value calculated using the standard gene, and the tumor size indicates the length of the long axis of the tumor)

[Equation 2]

BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0

BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤10

BCT score 10 if 0.8*U-BS - 13.71>10

As a result of comparing and analyzing the BCT score of the patient sample (n = 76) used for the analysis with the frequency of recurrence of distant metastases in each patient, as can be seen in Table 4 below, the higher the BCT score, the higher the frequency of recurrence of distant metastases. was found to increase.

In the pN2/3 breast cancer patient group, Cox hazard ratio analysis was performed to identify factors that correlate with the patient's prognosis, and factors that correlate with disease-free survival and distant metastasis-free survival were identified.

The results are shown in Tables 5 and 6 below.

Factors significantly correlated with prognosis derived from univariate and multivariate Cox hazard ratio analysis were BCT score and ER status (Allred Score). As the BCT score increased, the prognosis was significantly worse, and the ER status also tended to show a worse prognosis as the Allred score increased.

3. Threshold setting for risk group classification of patients with advanced breast cancer

As it was confirmed that the BCT Score correlates with the prognosis of pN2/3 patients through the above analysis results, risk group classification was performed by applying a new cut-off of the BCT Score. The risk group was classified into a low-risk group and a high-risk group, and the cut-off point of the BCT score was set as the point where the value of the hazard ratio for relapse between the low-risk group and the high-risk group was maximum.

As a result, when the high-risk group and the low-risk group were divided based on the BCT score 7, it was confirmed that there was a significant difference in the survival rate between the low-risk group and the high-risk group in both disease-free and distant metastasis survival rates (see Table 7 and FIG. 2).

A risk group with a significant difference in survival rate can be classified based on the BCT score of 7, but the purpose of prognostic diagnosis is to select a patient group with a good prognosis. An additional patient group was selected.

Considering the correlation between the BCT score and the relapse event, the cut-off to newly classify the group of 7 or less patients was set to 4.5. Therefore, since two cut-offs are applied to the patient group classification, a patient group with a BCT score of 4.5 and 7 was designated as an intermediate risk group and the survival rates of the patients were compared.

The results are shown in Table 8 and FIG. 3 below.

As a result of dividing the BCT Score threshold into three groups based on 4.5 and 7, the low-risk group, the medium-risk group, and the high-risk group, the 10-year disease-free and distant metastasis survival rate of the low-risk group was 91.7%, which was 74.3% of the medium-risk group and 74.3% of the high-risk group. It was confirmed that the survival rate was significantly higher than that of 35.3%.

Through the above results, the prognosis of advanced breast cancer patients (pN2 or pN3 breast cancer patients), and more specifically, disease-free / distant metastasis survival rate can be predicted very accurately with the BCT score and threshold value calculated according to Equations 1 and 2 above. can confirm that it is possible.

The method of the present invention has the effect of more accurately predicting the future metastasis, recurrence or metastatic recurrence of patients with advanced breast cancer, and the prognosis for disease-free/metastatic survival rate, suggesting a clue for the future direction of breast cancer treatment It can be usefully used for the purpose of

Claims (10)

In order to provide information necessary for predicting the prognosis of pN2 or pN3 advanced breast cancer patients, a breast cancer prognosis prediction method comprising the steps of:
(a) obtaining a biological sample from a breast cancer patient;
(b) UBE2C (Ubiquitin-conjugating enzyme E2C), TOP2A (Topoisomerase 2 alpha), RRM2 (ribonucleotide reductase M2), FOXM1 (Forkhead box M1), MKI67 (Marker of proliferation Ki-67) from the sample of step (a) And measuring the mRNA expression level of BTN3A2 (Butyrophilin subfamily 3 member A2);
(c) normalizing the mRNA expression level of the gene;
(d) evaluating the size of the tumor of the breast cancer patient;
(e) calculating the numerical value by substituting the standardized value in step (c) and the size of the tumor in step (d) into the following equations 1 and 2
[Equation 1]
Unscaled BCT score (U-BS) = a*△Ct_UBE2C + b*△Ct_TOP2A + c*△Ct_RRM2 + d*△Ct_FOXM1 + e*△Ct_MKI67 + f*△Ct_BTN3A2 + g*Tumor_size(cm) + h
[Equation 2]
BCT score = 0 if 0.8*Unscaled BCT score(U-BS) - 13.71 < 0
BCT score = 0.8*U-BS - 13.71 if 0 ≤ 0.8* U-BS - 13.71 ≤ 10
BCT score 10 if 0.8*U-BS - 13.71>10
(The value of the prognostic gene is a standardized mRNA expression value calculated using a standard gene, and the tumor size represents the length of the long axis of the tumor,
wherein a is 0.16 to 1.09, b is 0 to 0.71, c is 0 to 0.53, d is 0 to 0.57, e is 0 to 0.35, f is -1.02 to 0, g is 0.25 to 1.52, and h is 0.19 to 2.25 ); and
(f) If the value in step (e) is more than 7, the prognosis is predicted as a high-risk group with a poor prognosis, as a medium-risk group when the value is 4.5 or more and 7 or less, and when the value is less than 4.5, the prognosis is predicted as a low-risk group with a good prognosis step to do.
The method of claim 1, wherein the breast cancer prognosis is at least one selected from the group consisting of recurrence, metastasis, and metastatic recurrence.
The method of claim 1, wherein the breast cancer prognosis is disease-free survival, distant metastasis-free survival, or a combination thereof.
The method of claim 1 , wherein the breast cancer is estrogen receptor, progesterone receptor or estrogen receptor and progesterone receptor positive and HER2 negative breast cancer.
The method according to claim 1, wherein the standardization calculates the ratio to the average expression level of one or more standard genes selected from the group consisting of CTBP1 (C-terminal-binding protein 1), CUL1 (cullin 1), and UBQLN1 (Ubiquilin-1). A method characterized in that it is carried out by
The method of claim 1, wherein the sample is selected from the group consisting of a formalin-fixed paraffin-embedded (FFPE) sample of tissue containing cancer cells of a patient, fresh tissue, and frozen tissue. how to do it
According to claim 1, wherein the method for measuring the expression level of the gene is microarray, PCR (polymerase chain reaction), RT-PCR, quantitative RT-PCR (qRT-PCR), real-time polymerase chain reaction (real-time) PCR), Northern blot (northern blot), DNA chip and RNA chip, characterized in that any one method selected from the group consisting of.
The method according to claim 1, wherein the high-risk group is a group of patients requiring additional drug combination therapy in addition to standard drug treatment.
The method according to claim 1, wherein the medium-risk group is a group of patients requiring standard drug treatment.
The method according to claim 1, wherein the low-risk group is a group of patients that can selectively exclude or discontinue drugs during standard drug treatment.

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