CN114480652A

CN114480652A - Product for evaluating responsiveness of breast cancer patient to adjuvant endocrine therapy

Info

Publication number: CN114480652A
Application number: CN202210158071.9A
Authority: CN
Inventors: 饶皑炳
Original assignee: Shenzhen Luwei Biotechnology Co ltd
Current assignee: Shenzhen Luwei Biotechnology Co ltd
Priority date: 2022-02-21
Filing date: 2022-02-21
Publication date: 2022-05-13
Also published as: WO2023155471A1

Abstract

The invention discloses a product for evaluating the responsiveness of a breast cancer patient to auxiliary endocrine treatment. In a first aspect, the present application provides the use of an agent for detecting a gene in the manufacture of a product for assessing responsiveness of a breast cancer patient to adjuvant endocrine therapy, the gene comprising: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD 1. The gene model for assisting the responsiveness of endocrine treatment is established for a breast cancer patient, and can effectively reflect the response degree of a subject to the assisting endocrine treatment and effectively guide the postoperative treatment of the breast cancer patient.

Description

Product for evaluating responsiveness of breast cancer patient to adjuvant endocrine therapy

Technical Field

The application relates to the technical field of molecular diagnosis, in particular to a product for evaluating the responsiveness of a breast cancer patient to auxiliary endocrine treatment.

Background

The treatment of breast cancer requires first a diagnosis by means of basic pathology, which is mainly a clinical index including definition of lesion size, histological type, histological grade, presence or absence of vessel invasion, presence or absence of in situ cancer, margin of cut, lymph node condition, etc., and molecular pathology, which includes detection of molecular indices of all invasive lesions. Molecular markers include ER (estrogen receptor), PR (progestogen receptor), HER2 (human epidermal growth factor receptor 2), and Ki-67 (tumor cell proliferation index), where ER and PR may be collectively referred to as Hormone Receptor (HR), and HR +/HER 2-type breast cancer patients account for 60% of total breast cancer patients, and although most early patients are cured by surgery, some patients still have local recurrence or distant metastasis after surgery. Therefore, in order to determine the prognosis of a patient more effectively, in addition to conventional clinical pathology and molecular indicators, it is necessary to find a suitable molecular indicator, such as a transcriptome polygene detection indicator, which is widely used at present, starting from the gene expression abnormality.

The polygenic prognosis of breast cancer dates back to at least the 70 gene detection system (Mammaprint) developed by researchers at the netherlands cancer institute in 2002, which is the first polygenic detection system approved by the FDA in the united states for clinical use. Besides, the gene detection system for breast cancer 21 (Oncotype DX), nanopore-based 50 gene detection (PAM50), Breast Cancer Index (BCI), and the like, which are marketed by american Health corporation in 2005, have been generally used internationally. Endocrine therapy is generally used as a basic framework of the treatment scheme of HR +/HER2 early breast cancer population in the breast cancer treatment guidelines, so that the evaluation of the sensitivity to endocrine therapy is of great practical significance: on the one hand, for patients with high responsiveness to endocrine therapy, confidence in the basic treatment regimen is established; on the other hand, for patients with low responsiveness, doctors may be prompted to consider the feasibility of adding other adjunctive therapies to the endocrine therapy as soon as possible. However, most of the above methods for determining the risk of recurrence by assessing the risk of recurrence to determine whether the HR +/HER 2-early breast cancer population needs additional adjuvant chemotherapy after endocrine therapy, and there is no suitable method for assessing the sensitivity and responsiveness of patients to endocrine therapy per se, so it is necessary to provide such methods to effectively complement the existing risk assessment of recurrence.

Disclosure of Invention

The present application is directed to solving at least one of the problems in the prior art. To this end, the present application proposes a product for assessing the responsiveness of breast cancer patients to adjuvant endocrine therapy.

In a first aspect of the application, there is provided the use of an agent for detecting a gene in the manufacture of a product for assessing responsiveness of a breast cancer patient to adjuvant endocrine therapy, the gene comprising: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer selected from 1-23.

According to the application of the embodiment of the application, at least the following beneficial effects are achieved:

the gene model for the responsiveness to auxiliary endocrine treatment is established for the breast cancer patient population, the expression level of the relevant gene of the breast cancer patient is obtained through a detection reagent, and the gene model can effectively reflect the response degree of a subject to the auxiliary endocrine treatment, so that the postoperative treatment of the breast cancer patient is effectively guided.

Among them, U2AF2(U2 Small Nuclear RNA Autoxiliary Factor 2) is the U2 Small Nuclear RNA cofactor 2 gene, which encodes proteins that regulate the process of mRNA molecule translocation from the nucleus to the cytoplasm by preventing translocation of uncompleted spliced pre-mRNA with introns from the nucleus and facilitating translocation of spliced mRNA entirely by exons. In addition, related reports show that U2AF2 is related to the generation and the progression of various tumors such as non-small cell lung cancer and the like.

CREBBP (CREB Binding protein) is a cAMP response element Binding protein gene that is widely expressed and involved in transcriptional co-activation of a number of different transcription factors. The gene plays a key role in embryonic development, growth control and homeostasis by combining chromatin remodeling with transcription factor recognition. The protein coded by the gene has histone acetyltransferase activity, can acetylate histone and non-histone, and provides a specific label for transcriptional activation.

LATS1(Large Tumor Suppressor Kinase 1) is a Large Tumor Suppressor Kinase 1 gene whose encoded protein is a serine/threonine Kinase that is localized to the mitotic apparatus early in mitosis and complexed with the cell cycle controller CDC2 Kinase. Meanwhile, as a negative regulator of YAP1 in Hippo signaling pathway, it plays a key role in organ size control and tumor suppression by limiting proliferation and promoting apoptosis.

TOP2A (DNA Topoisomerase II Alpha) is a topogram of DNA Topoisomerase IIalpha gene that regulates DNA replication or transcription. The anthracycline chemotherapeutic drug takes TOP2A as a target, and inhibits the expression of TOP2A to weaken the recombination effect mediated by TOP2A when DNA uncoils, so that the DNA is broken, programmed cell death is started, and the aim of removing cancer cells is fulfilled.

SMARCB1(SWI/SNF Related, Matrix Associated, active Dependent Regulator Of Chromatin, Subfemity B, Member 1) is a SWI/SNF-Related, Matrix-Related, chromosomal Actin-Dependent Regulator Subfamily B Member 1 gene, which encodes a protein that is part Of a complex that relieves Chromatin structure repression, allowing the transcription machinery to more efficiently access its target. Meanwhile, the gene is also a tumor suppressor gene, and the mutation of the gene is related to malignant rhabdoid tumor.

GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) is Glyceraldehyde-3-Phosphate Dehydrogenase gene, and the encoded protein is a typical moonlight protein, and is involved in controlling mRNA stability, apoptosis, gene transcription, DNA stability maintenance, nuclear tRNA efflux and the like besides participating in energy production, membrane fusion, endocytosis and iron ion channels. Additional studies have shown that GAPDH has an important role in tumor growth and is associated with poor prognosis.

TRAF1(TNF Receptor Associated Factor 1) is a TNF Receptor-Associated Factor 1 gene, and the TRAF protein family to which its encoded protein belongs binds to various receptors of the TNFR superfamily and mediates signal transduction. The heterodimer complex formed by the protein and TRAF2 is necessary for TNF-alpha mediated activation of MAPK8/JNK and NF-kappa B, and interacts with apoptosis inhibitory proteins (IAPs) so as to mediate anti-apoptosis signals from TNF receptors.

GUSB (glucuronidase beta) is a glucuronidase beta gene that encodes a hydrolase enzyme capable of degrading glycosaminoglycans including heparan sulfate, dermatan sulfate and chondroitin sulfate.

TNFSF8(TNF Superfamily Member 8) is a TNF Superfamily Member 8 gene, and its encoded protein is a ligand of Tumor Necrosis Factor (TNF) receptor CD30, also known as CD30L/CD 153. CD30 is expressed in activated T cells, NK cells, and B cells; TNFSF8 is expressed on activated T cells, resting B cells, and macrophages.

UPB1 (Beta-uretroprionase 1) is the β -Ureidopropionase 1 gene. Beta-ureidopropionases are involved in the pyrimidine degradation pathway. Uracil and thymine are degraded by the sequential action of dihydropyrimidine dehydrogenase, dihydropyrimidines, and β -ureidoalanases on β -alanine and β -aminoisobutyric acid. In some solid tumors, pyrimidine catalyzed degradation maintains an EMT-driven mesenchymal-like state, thereby affecting cancer metastasis.

MMP7(Matrix Metallopeptidase 7) is a Matrix Metallopeptidase 7 gene, the protein encoded by which is a member of the Matrix Metalloproteinase (MMPs) peptidase M10 family and is involved in the breakdown of extracellular Matrix and basement membrane proteins in normal physiological processes, such as fibronectin, collagen type IV, laminin, and in particular elastin, pentosan, osteopontin, and chondroprotein-polysaccharide aggregates, and the like. It is also involved in promoting tumor cell invasion and progression, and exhibits high expression in various cancers.

DES (Desmin) is a desmin gene, which encodes a protein that is the major protein component of Solitary Fibrous tumors (solid Fibrous Tumor) in the breast or elsewhere.

SRD5a2(Steroid 5Alpha-Reductase 2) is a Steroid 5Alpha-Reductase 2 gene encoding microsomal proteins that are expressed at high levels in androgen sensitive tissues. The encoded protein has activity at acidic pH and is sensitive to the 4-azasteroid inhibitor finasteride. It is, like the Androgen Receptor (AR), a decisive regulator of androgens.

S100A9(S100 Calcium Binding Protein A9) is the S100 Calcium Binding Protein A9 gene, which encodes a Protein that is a member of the S100 Protein family. The S100 protein is localized in the cytoplasm and/or nucleus of various cells and is involved in the regulation of many cellular processes, such as cell cycle progression and differentiation. This protein may play a role in inhibiting casein kinase and altered expression may be associated with cystic fibrosis.

KIT (KIT Proto-Oncogene, Receptor Tyrosine Kinase) is a KIT Proto-Oncogene Receptor Tyrosine Kinase gene, and its encoded protein phosphorylates a plurality of intracellular proteins after being activated by its cytokine ligand Stem Cell Factor (SCF), playing a role in proliferation, differentiation, migration, and apoptosis of various cell types.

ESR1(Estrogen Receptor 1) is an Estrogen Receptor 1 gene, whose encoded protein is localized to the nucleus, is capable of forming dimers with Estrogen Receptor 2, regulates the transcription of a number of Estrogen-induced genes that play a role in growth, metabolism, sexual development, pregnancy and other reproductive functions, and plays a key role in breast cancer, endometrial cancer and osteoporosis.

TSPYL5(Testis-Specific Y-Encoded-Like Protein 5) is a Testis-Specific Y-Encoded-Like Protein 5 gene, and is possibly involved in regulating cell growth by regulating Akt signal pathway, and is involved in regulation of p53/TP53 by inhibiting p53/TP53 Protein level and promoting ubiquitination thereof.

NR4A1(Nuclear Receptor subunit 4Group A Member 1) is a Nuclear Receptor Subfamily 4A Group Member 1 gene, and the encoded protein acts as a Nuclear transcription factor, and the transfer of the protein from the nucleus to the mitochondria induces apoptosis.

Was (wasp action Nucleation factor) is a Wiskott-Aldrich syndrome protein Actin Nucleation Promoting factor gene, the protein family of which encodes proteins involved in signal transduction from cell surface receptors to the Actin cytoskeleton, and is associated with the small gtpase Cdc42 and the cytoskeletal tissue complex Arp2/3, which are known to regulate Actin filament formation.

EP300(E1A Binding Protein P300) is the E1A Binding Protein P300 gene which encodes the adenovirus E1 a-associated cell P300 transcriptional coactivator Protein. The protein acts as a histone acetyltransferase, regulates transcription through chromatin remodeling, and plays an important role in cell proliferation and differentiation, and mediates CAMP gene regulation by specifically binding to phosphorylated CREB proteins.

TPX2(TPX2 Microtubule Nucleation Factor) is a TPX2 Microtubule Nucleation Factor gene and plays a vital role in spindle assembly in the process of cell mitosis.

VHL (Von Hippel-Lindau Tumor supressor) is a Von Hippel-Lindau cancer Suppressor gene, and the protein encoded by the gene is involved in ubiquitination and degradation of Hypoxia Inducible Factor (HIF), thereby affecting cell metabolism and differentiation.

GLUD1(Glutamate Dehydrogenase 1) is Glutamate Dehydrogenase 1 gene, and the encoded protein of the gene is a mitochondrial matrix enzyme, can catalyze glutamic acid oxidative deamination to generate alpha-ketoglutarate and ammonia, and plays an important role in regulating amino acid-induced insulin secretion. And is allosterically activated by ADP, and inhibited by GTP and ATP.

Adjuvant therapy is also called adjunctive therapy, and refers to a therapeutic means for eliminating residual cancer cells in vivo given after surgery, and reduces the possibility of tumor recurrence, metastasis and spread by adjuvant therapy. The auxiliary endocrine treatment in the embodiment of the application refers to endocrine treatment given after the operation of a breast cancer patient, and the endocrine treatment method comprises the step of removing the stimulation of hormone on tumor cells by means of medicines or endocrine gland excision so as to play an anti-tumor role. Traditional endocrine treatment drugs include selective estrogen receptor inhibitors (such as tamoxifen, also known as tamoxifen), selective estrogen receptor downregulators (such as fulvestrant), aromatase inhibitors (such as letrozole, anastrozole, exemestane), etc., and as technological approaches advance, targeted treatment with combination of two or more drugs can be included. The responsiveness of breast cancer patients to endocrine therapy in the examples of this application refers to the sensitivity of all early breast cancer patients to HR +/HER2-, without lymph node metastasis to endocrine therapy, whether the disease progression or cure can be delayed by endocrine therapy, but no other factors such as age, menopause, or which endocrine therapy regimen is considered.

In some embodiments of the present application, the gene comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty-one, at least twenty-two, all twenty-three of the groups.

In some embodiments of the present application, the agent detects mRNA expression levels of the gene.

In some embodiments of the present application, the breast cancer patient is molecularly typed as HR positive HER2 negative. Wherein HR positive means that at least one of ER (estrogen receptor) and PR (progesterone receptor) is positive.

In some embodiments of the present application, the primary tumor stage of a breast cancer patient is T1-T2 and the regional lymph node stage is N0-N3.

Wherein, the primary tumor stage is T stage judged according to TNM stage rule, and specifically can be confirmed according to clinical and/or influential diagnosis means or according to pathological size and range, T1 represents that the maximum diameter of the tumor in mammary gland is 20mm or less, and T2 represents that the diameter of the tumor is more than 20mm but not more than 50 mm. The comprehensive T1-T2 indicates that the maximum diameter of the tumor in the breast does not exceed 50 mm. Lymph nodes that are predominantly located in the axilla, above and below the clavicle and below the sternum in breast cancer patients are called regional lymph nodes, while those elsewhere in the body are called distant lymph nodes. Regional lymph node staging is staging for metastasis and spread of cancer cells in lymph nodes, N0 is a population of tumor cells with no evidence of regional lymph node metastasis or only isolation, N1 meets the criteria that the cancer has metastasized to 1 to 3 axillary lymph nodes, at least 2mm in diameter, etc., N2 can be divided into N2a (e.g., the cancer has spread to 4 to 9 axillary lymph nodes) and N2b (e.g., the cancer has spread to the intra-mammary lymph nodes and not to the axillary lymph nodes), N3 can be divided into N3a (e.g., the cancer has spread to 10 or more axillary or subclavian lymph nodes), N3b (e.g., the cancer has spread to the intra-and axillary lymph nodes) and N3c (e.g., the cancer has spread to the supraclavicular lymph nodes).

In some embodiments of the present application, the regional lymph node stage is N0.

In a second aspect of the present application, there is provided a kit for evaluating responsiveness of a breast cancer patient to adjuvant endocrine therapy, the kit comprising a reagent for detecting a gene comprising: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer selected from 1-23.

In some embodiments of the present application, the breast cancer patient is molecularly typed as HR positive HER2 negative.

In a third aspect of the present application, a computer-readable storage medium is provided that stores computer-executable instructions for causing a computer to:

step 1: obtaining information on the expression level of a gene in a sample from a breast cancer patient, the gene comprising: at least N of the group consisting of U2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer from 1 to 23;

step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the responsiveness of a breast cancer patient to adjuvant endocrine therapy.

In some embodiments of the present application, the regional lymph node staging is N0.

In some embodiments of the present application,

a_iis the expression level of the gene, b_iThe weight of the gene is set, and n is the number of the genes.

In some embodiments of the present application, when the score is above the set value, the breast cancer patient is indicated to have high responsiveness to endocrine treatment post-operatively.

In some embodiments of the present application, the score is 0.2492 × U2AF2+0.2013 × CREBBP +0.2007 × LATS1+0.1235 × TOP2A +0.1175 × SMARCB1+0.1155 × GAPDH +0.1155 × TRAF1+0.1111 × GUSB +0.0969 × TNFSF8+0.045 × UPB1+0.0412 × MMP7+0.0242 × DES-0.0306 × SRD5a2-0.0315 × S100a 9-63 0.0368 × KIT-0.0595 × ESR1-0.0711 × TSPYL5-0.0765 × NR4a1-0.182 × WAS-0.1922 × EP300-0.1922 × TPX2-0.2026 × VHL-0.2642 × gld 1, and the abbreviations for genes in the formulae indicate the expression levels of the corresponding genes.

In a fourth aspect of the present application, an electronic device is provided, which includes a processor and a memory, the memory storing a computer program executable on the processor, the processor implementing the following operations when executing the computer program:

step 1: obtaining information on the expression level of a gene in a sample from a breast cancer patient, the gene comprising: u2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer selected from 1-23;

In some embodiments of the present application,

The memory, as a non-transitory computer-readable storage medium, may be used to store a non-transitory software program and a non-transitory computer-executable program, such as the process of assessing responsiveness of a breast cancer patient to an auxiliary endocrine therapy as described in embodiments of the present application. The processor enables assessment of the responsiveness of a breast cancer patient to ancillary endocrine therapy by executing a non-transitory software program and instructions stored in memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data for performing the marker screening method described above. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device.

In some embodiments of the present application, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Non-transitory software programs and instructions needed to implement the above described evaluation are stored in memory and, when executed by one or more processors, perform the above described evaluation.

The above described implementation of the apparatus is merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It will be understood that all or some of the steps, systems disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). It should be understood that computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer.

In addition, it will be understood that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.

The scoring model for endocrine treatment response in breast cancer patients as described above is further discussed below:

the model relates to 23 genes, and the weight is as follows: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD 1. These genes can be simply classified into:

transcription regulation: u2AF2, CREBP, EP300, ERS1, VHL;

cell cycle regulation, cell proliferation and differentiation: SMARCB1, S100a9, KIT;

and (3) apoptosis: LATS1, MMP7 associated with the Hippo signaling pathway, TRAF1 associated with the TNF-a pathway;

cytoskeleton and tissue architecture: GAPDH, DES, WAS, TPX 2.

Hormones: SRD5a2, ESR 1; and so on.

The expression or mutation of U2AF2 can be related to the occurrence, development, recurrence and metastasis of cancers such as non-small cell lung cancer, prostatic cancer and the like, and the weight of U2AF2 in the model is the largest, which indicates that the high expression of U2AF2 has great promotion effect on endocrine treatment.

CREBP in breast cancer is involved in estrogen receptor signaling, and enhances the transcription and DNA binding activity of the breast cancer by inducing the acetylation of estrogen receptors. Pathological research of breast cancer finds that CREBP in HR + breast cancer shows high expression and corresponds to better five-year DFS. However, no high expression of CREBBP was shown in HER2+ breast cancer, suggesting that CREBBP may also be involved in HER2 signaling pathway. The second largest weight assigned to CREBP in the model indicates that the CREBP high expression has a great promotion effect on the endocrine treatment effect. The closely related ESR1, with a negative weight in the model, indicates the negative effect of ESR1 on the effectiveness of endocrine therapy, which is also consistent with a number of ESR1 functional studies.

The acetylase EP300 structure functions similarly to CREBBP, but in the endocrine therapy response model, it is weighted negative and the absolute value is larger, in contrast to CREBBP, indicating that EP300 plays a drug-resistant role in the endocrine therapy response. EP300, as a beta-catenin transcription co-activator, participates in a Wnt pathway and promotes the growth of tumor stem cells (CSC), and case studies of triple negative breast cancer and basal-like breast cancer show that EP300 down-regulates a breast cancer resistance gene ABCG2, promotes the CSC phenotype, and promotes the tumor growth and metastasis. The results of a study of chemogenic (metaplastic) breast cancer showed that EP300 and E-cadherin expression was down-regulated, thereby initiating EMT and promoting resistance to paclitaxel. These results indicate that the mechanism of resistance of EP300 to endocrine therapy may be consistent with chemotherapy.

The cancer suppressor gene Von Hippel-Lindau (VHL) regulates the aggregation of Hypoxia Inducible Factor (HIF) on the one hand, thereby influencing cell metabolism and differentiation, including GLUT-1, 6-PFK and PDK related to glycometabolism; angiogenic VEGF, PDGF, CTGF; CA9 to control extracellular pH; TGFa; erythropoiesis-related genes; CXCR4 and its ligand SDF1, which stimulate chemotaxis, whereas CXCR4 is associated with tumor metastasis; EMT-related MMPs 1, LOX, TWIST, and HGFR. On the other hand, independent of HIF, VHL is also involved in collagen type iv COL4, fibronectin FN1, motor protein KIF 2; cytoskeleton-associated aPKC, PAR3/6, GSK 3B; apoptosis-related P53, MDM2, JUNB; cell survival CK2, NFKCB, CARD 9; cell cycle CDKN 1B; protein ubiquitinated TCEB 1/2. VHL was negative in weight and large in absolute value in this model, indicating that it has some negative impact on endocrine therapy effectiveness.

The protooncogene KIT has been extensively studied in the field of breast cancer. Immunohistochemical staining results of pathological sections of invasive ductal breast cancer showed that 75% of the samples had low KIT expression, which was associated with the number of spread lymph nodes and poor DFS. Immunohistochemical staining of triple negative breast cancer was in contrast, whereas estrogen promoted KIT expression in invasive lobular breast cancer models leading to an aggressive phenotype. The weight of KIT in the model is negative, which indicates that KIT has negative influence on the effectiveness of endocrine therapy and is consistent with the function of KIT in promoting tumor invasiveness.

LATS1 is a negative regulator of YAP1 in the Hippo signaling pathway and plays a key role in organ size control and tumor suppression by limiting proliferation and promoting apoptosis. Cancer cell plasticity increases following LATS1 knock-out, luminal B cancers tend to express basal-like features more and increase resistance to endocrine therapy. In ER + cells, CRABP binding to LATS1 stops LATS1 ubiquitination, thereby activating YAP1/TAZ, initiating the Hippo signaling pathway, inhibiting invasiveness and metastasis; in contrast to ER-cells, CRABP binding to LATS1 initiates LATS1 ubiquitination, which down-regulates YAP1/TAZ, terminates the Hippo signaling pathway, and activates ER-cell invasiveness and metastasization. In contrast, Moroishi T found in a breast cancer cell model that the knockout of LATS1/2 increased cancer immune response, thereby inhibiting tumor growth. The weight of LATS1 is more positive in this model, indicating that LATS1 plays a significant positive role in endocrine therapy.

MMP7 is another gene associated with the Hippo signaling pathway in the model. Gene expression analysis shows that YAP1/MMP7/CXCL16 gene axis expression is strongly related to Hippo-YAP1 pathway kinase LATS 2. High YAP1 expression resulted in high MMP7 expression, thereby down-regulating the chemoattractant CXCL 16. Expression of soluble CXCL16 on tumor tissue can attract immune cells with its receptor CXCL6, such as CD4+ or CD8+ T cells and NK cells, thereby provoking an immune response, so high expression of YAP1/MMP7 prevents penetration and accumulation of immune cells on tumor tissue, resulting in immune escape. Treatment of MDA-MB-231 with MMP7 inhibitor resulted in loss of invasiveness and growth retardation of the cells. While MMP7 was weighted positively in this model, indicating that MMP7 has a positive effect on endocrine therapy.

GUSB has two diametrically opposite effects of promoting and inhibiting cancer in tumors of different invasiveness, respectively, in an "S-score" that measures cancer susceptibility, defined by combining gene mutation, methylation, gene fold Change (CNV) and gene expression data. In the breast estrogen balance mechanism, GUSB regulates the hydrolysis of estrogen sulfate and glucuronide, regulates the biological conversion of 17 beta-estradiol and estrogen, and is a direct factor of breast cancer risk. The weight of GUSB7 in the model is positive, which indicates that GUSB has positive effect on endocrine therapy.

The genes related to NF-bK pathway and MAPK pathway in the model are TNFSF8 and TRAF 1. TNFSF8 is a ligand of Tumor Necrosis Factor (TNF) receptor CD30, also called CD30L, the high expression abnormality of CD30 is a marker of hematopoietic system malignant tumor including anaplastic large cell lymphoma and Hodgkin's lymphoma, and TRAF1 is a downstream molecule of CD30/CD30L signaling. In a retrospective study of 284 breast cancers, no ALK-positive or CD 30-positive examples were found in the IHC staining of the pathological sections, perhaps indicating a lack of immune cells in the cancer microenvironment. The weights of TNFSF8 and TRAF1 were positive in the model, indicating that CD30/CD30L/TRAF1 signaling plays a positive role in endocrine therapy.

SMARCB1 is the core subunit of the chromatin remodeling SWI/SNF complex, a highly conserved global transcriptional regulator that regulates target gene expression by recruiting transcription factors to the target gene, or by altering nucleosome location. The absence of SMARCB1 was the primary driver of malignant rhabdoid tumors, leading to the termination of the G0/G1 cell cycle switch; a false activation sonic hedgehog pathway (SHH) and a Wnt/beta-catenin pathway; abnormal expression of embryonic stem cell renewal genes and nerve growth genes. The weight of SMARCB1 in the model was a large positive number, indicating that SMARCB1 plays a significant positive role in the response to endocrine therapy for breast cancer, the mechanism of which remains to be studied.

The DNA topoisomerase TOP2A is a well-known important gene, regulating the topogram of DNA replication or transcription. TOP2A amplification was frequently accompanied by HER2 amplification, both co-amplified accounting for 40-50% of HER2+ breast cancer. The study found that HER2 and TOP2A amplified simultaneously breast cancer are also accompanied by high expression of other genes: CASC3, CDC6, RARA, and SMARCE 1. While SMARCE1 is similar to the SMARCB1 gene in the model described above. In addition, TOP2A gene expression was strongly correlated with DMFS, and was associated with complete remission after anthracycline chemotherapeutic Cyclophosphamide (Cyclophosphamide) treatment. TOP2A was weighted more positively in the model, indicating that TOP2A plays a significant positive role in the response to endocrine therapy for breast cancer.

The expression research of GAPDH in breast cancer shows that the OS and RFS of GAPDH high-expression people are greatly reduced; when the breast cancer cells MCF7 are cultured by estradiol, the expression level of GAPDH is in positive correlation with the estradiol dosage, which indicates that GAPDH is related to the growth of the cancer cells and the malignancy degree of the breast cancer. Relevant evidence also suggests that GAPDH has an important role in tumor growth and is associated with a poor prognosis. GAPDH was weighted more positively in the model, indicating a positive effect on the response to endocrine treatment of breast cancer.

DES encodes Desmin (Desmin), a major protein component of isolated Fibrous tumors (solid Fibrous tumors) of the breast or other sites. Isolated fibrous tumors are less than 0.2% in patients with breast cancer and are mostly benign. DES was weighted least positive in the model, indicating a less positive effect on the response to endocrine therapy for breast cancer.

Beta-ureidopropionase UPB1 is involved in pyrimidine metabolism, while in some solid tumors, the catalytic degradation of pyrimidine maintains an EMT-driven mesenchymal-like state, with an impact on cancer metastasis. UPB1 is also involved in the metabolism of fluoropyrimidine chemotherapeutics such as 5-FU, and UPB1 was positively weighted in the model, indicating a positive effect on the response to endocrine therapy for breast cancer.

The estrogen receptor ESR1 is a marker gene for breast cancer, and ESR1 is weighted as a negative integer in the model, indicating a negative effect on the response to endocrine therapy for breast cancer. Another gene, SRD5A2, is similar to the Androgen Receptor (AR) and is the determinant regulator of androgen, and the SRD5A2 weights in the model are likewise negative integers, indicating a negative response to endocrine therapy for breast cancer.

The calcium binding proteins S1008A and S1009A belong to damage-associated molecular pattern (DAMP) molecules, regulate and control immune response of tumor microenvironment to promote tumor growth and malignant transformation, have functions similar to cytokines and chemokines, and play double roles of inhibiting and assisting cancer between tumors and inflammations. S1009A was weighted as a negative integer in the model, indicating a negative effect on the response to endocrine treatment of breast cancer.

The testis-specific Y-encoded-like protein TSPYL5 inhibits the cancer suppressor gene P53 by down-regulating USP7, thereby promoting the growth of cancer. A post-menopausal early ER + breast cancer sample research shows that high expression of TSPYL5 leads to high expression of CYP19A1, indicating that TSPYL5 is possibly a transcription factor involved in regulating CYP19A1 and other genes. While TSPYL5 was weighted as a negative integer in the model, indicating a negative effect on the response to endocrine treatment for breast cancer.

The microtubule nucleating factor TPX2 is critical for spindle assembly during cell mitosis. Some studies have shown that Chromosome Instability (CIN) cells survive by activating TPX2/AURKA, with high expression of TPX2 present in many cancers. TPX2 was weighted as a negative integer and larger in absolute value in the model, indicating a significant negative effect on the response to endocrine therapy for breast cancer.

The NR4A receptor acts as a transcription factor, altering the expression of downstream genes in multiple functions of cells: apoptosis, proliferation, DNA repair, metabolism, cell migration, inflammation, and angiogenesis. NR4A1 is used as a potent activator of TGF-b signal, and can promote AXIN 2-RNF 12/ARKADIA-induced SMAD7 degradation by combining SMAD7 with AXIN2, thereby starting TGF-b signaling and promoting tumor metastasis. Inflammatory cytokines lead to strong expression of NR4a1, and thus high expression of NR4a1 in breast cancers with higher immune infiltration and corresponding poorer prognosis. This is consistent with the negative outcome of NR4a1 in the model, which is weighted negative, in response to endocrine therapy.

The interaction of tumor-associated macrophages (TAM) with tumor cells promotes tumor invasion and metastasis, and paracrine closed loops are formed by binding of colony stimulating factor-1 expressed on TAM to EGFR expressed by tumor cells. Deletion of the WAS gene family WASP attenuates this paracrine effect, preventing the co-migration of TAMs with cancer cells and attenuating metastatic spread. WAS weights in the model are negative integers and large absolute values, indicating a large negative effect on the response to endocrine therapy for breast cancer.

Glutamate dehydrogenase GLUD1 is the largest negative integer in weight in the model, indicating that GLUD1 has the greatest negative effect on the response to endocrine therapy for breast cancer.

It will be appreciated that the above discussion is based on a model of all 23 genes, each of which has been studied more or less in the field of breast cancer to show the relevant effect of these genes on breast cancer, but in the examples of the present application, it is not limited to a model of all 23 genes, and that selecting several genes from them can also construct other models with a response evaluation effect.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

Fig. 1 is ROC curves corresponding to AUC maximum, median and minimum values of 20 replicates of the optimal model cross validation of the present application.

FIG. 2 is a ROC curve that the optimal model of the present application verifies across all samples.

FIG. 3 is a box plot of the expression levels of individual genes in the optimal model of the present application in different populations.

FIG. 4 is a ROC curve for a marker for a single gene in the optimal model of the present application, where the 0-expression values indicate that the gene is negatively correlated with endocrine therapy response.

Fig. 5 shows the results of maximum, median, and minimum values of cross-validated AUC of multiple gene subsets in the genes of the optimal model of the present application. Wherein a and b are the cross-validation results of the 2 gene model, and c and d are the cross-validation results of the 22 gene model.

Detailed Description

The conception and the resulting technical effects of the present application will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all embodiments, and other embodiments obtained by those skilled in the art without inventive efforts based on the embodiments of the present application belong to the protection scope of the present application.

The following detailed description of embodiments of the present application is provided for the purpose of illustration only and is not intended to be construed as a limitation of the application.

In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present number, and the above, below, within, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

In the description of the present application, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Example 1

In this example, a set of breast cancer gene markers screened by using mRNA gene expression data is prepared as follows:

first, data set preparation

Data set genechip data for breast cancer sections (Affymetrix platform) cancer genomic map TCGA data set: only in 258 cases of TCGA-BRCA, ER + or PR +, HER2-, Node-0 early (T1-T2) breast cancer patients were selected and treated by endocrine therapy. Among them, TCGA-BRCA has 78 responses and 180 no responses. After gene transcription with extremely low expression (defined as that the number of non-zero expression samples is not more than 10) is eliminated, miRNA and lncRNA are eliminated, and the gene factor is 9524.

Data standardization was performed step by step on samples and genes:

(1) normalization was performed for each sample: respectively calculating the median of all gene expression levels of each sample, then subtracting the median of all gene expression levels of the sample from the original expression level of each gene of the sample to obtain the expression level of each gene of the sample after primary standardization, and removing the difference of mRNA input levels of the sample by the standardization mode;

(2) normalization was performed for each gene: and (2) further calculating the median of the expression quantity of each gene in all samples based on the gene expression data subjected to primary normalization in the step (1), and subtracting the median of the expression quantity of each gene in all samples from the expression quantity of each gene subjected to primary normalization in each sample to obtain the expression quantity of each gene subjected to secondary normalization in each sample.

Second, screening and model of gene diagnosis marker

For breast cancer patient RESPONSE to adjuvant endocrine therapy (RESPONSE), a model was established by:

determining genes associated with responsiveness to adjuvant endocrine therapy. And (3) searching genes with statistical significance (p is less than 0.05) capable of distinguishing different target variable populations (with response vs and without response) by using a t-test (t-test), and preliminarily obtaining the differentially expressed genes. Specifically, the treatment response target variable population is:

(ii) responsive population (Response ═ 1): including patients who are clinically evaluated as complete remission, or partial remission, or stable disease after return visit after treatment;

non-responsive population (Response ═ 0): patients with disease progression.

Here, the

And (3) complete alleviation: all cancers or tumors disappeared; there is no evidence of disease. Tumor markers (as applicable) may be within the normal range.

Partial mitigation: cancer shrinks by a percentage, but disease remains. Tumor markers (as applicable) may have declined, but evidence of disease still exists.

Stable disease condition: cancer neither grows nor shrinks; the number of diseases did not change. Tumor markers (as applicable) did not change significantly.

Disease progression: cancer has developed; more diseases are present than before treatment. Tumor marker testing (as applicable) showed elevated tumor markers.

Genes were up-or down-regulated into groups. The differential expression genes are divided into two groups, and t in the t-test result is a positive number representing the genes which are expressed in the tissues of the patient and are down-regulated; t is negative and represents a gene whose expression is up-regulated in the tissues of the patient. And respectively carrying out hierarchical association coefficient analysis on the two groups of genes.

And (5) analyzing a hierarchical association coefficient. Genome with up-or down-regulated expressionThe hierarchical association coefficient clustering is carried out respectively, the purpose is that genes in each cluster need to be approximately associated with each other pairwise at a given association coefficient level, the clustering is carried out through the following iteration, firstly, an association coefficient matrix between pairwise genes in an up-regulated or down-regulated genome is obtained, and a first association coefficient threshold value T is set₁0.75 (note: this threshold can be adjusted by looking at the distribution of the correlation coefficients between all pairs of genes beforehand), the correlation coefficient matrix is scanned, and all genes larger than the threshold T are recursively clustered as follows: firstly, sorting the corresponding T-test results of the genes from small to large according to the p value, taking the first gene which is not classified as a candidate gene, classifying all the genes with the correlation coefficient larger than T and the candidate gene into a cluster, then taking the row (or column) average value of a correlation coefficient submatrix formed by the clustered genes, sorting the genes from large to small according to the average value, and taking the first gene (namely the gene with the maximum correlation coefficient in the cluster) as the representative gene of the cluster, namely the gene most related to all the genes in the cluster; adjusting the threshold value down by 0.05 to obtain a second correlation coefficient threshold value T₂＝T₁-0.05, repeating the above steps for the remaining genes not included in the cluster until all genes are exhausted, and allowing the differentially expressed genes to be included in the cluster in their entirety, the representative genes of each cluster constituting the model candidate genes for the marker.

Iterative linear regression analysis determined the genome. In the hierarchical correlation coefficient analysis, the number of genes(s) as model parameters is predetermined, and iterative linear regression analysis is performed. And recycling different s values, searching the number of the optimal model parameter values, and determining the optimal model according to the maximum value of the corresponding R square value (rsq).

The final endocrine treatment response score was 23 gene model: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD 1.

The parameters of each gene in the final optimal model are shown in table 1 below:

TABLE 1.23 correlation parameters of the genes in the Gene Linear regression model

And (3) cross validation: and (3) averaging the data set according to the population of the target variable, wherein half of the data set is a training set, the other half of the data set is a verification set, ROC and AUC are calculated, and the operation is repeated for N times (20). And calculating the statistical characteristics of the AUC, such as minimum, maximum, median. The median AUC of the cross-validation was used as an indicator for evaluating the model results.

The results are shown in figure 1, and it can be seen from the figure that the maximum value of AUC of the model after 20 times of repetition is 0.9, the minimum value is 0.7, and the median value is 0.86, which indicates that the model has good classification significance, and can well separate people with different response sensitivities to auxiliary endocrine therapy in breast cancer patients with HR +/HER2-, T1-T2, and N0, so as to effectively guide postoperative auxiliary therapy of the breast cancer patients.

The Response of patients with established 23-gene linear regression models (score 0.2492 × U2AF2+0.2013 × CREBBP +0.2007 × LATS1+0.1235 × TOP2A +0.1175 × SMARCB1+ 1 × GAPDH + 1 × TRAF1+0.1111 × GUSB + 1 × TNFSF 1+ 0.045 × UPB1+0.0412 × MMP 1+ 0.0242 × DES-0.0306 × SRD5a 1-0.0315 × S100a 1-1 × KIT-1 × ESR 1-1 × tsl 1-1 × NR4a1-0.182 × WAS S-1 × EP 300-1 × TPX 4-VHL-1 × 1) to the data in tctctctcc-breast cancer dataset WAS plotted as a Response to total endocrine Response curve (total Response curve) of patients with total endocrine Response curve of 1+ 1 × nether 1+ 1 × 1, no Response curve plotted as no Response to total endocrine Response to the Response curve of the tcc 1-685 + 1 × 1 (total endocrine Response curve) in the total Response curve of patients with No. 387 + 1 × nether 1 × 1). The AUC was 0.9281, and the optimal decision point on the ROC curve (shown as a dashed line) corresponded to 89% specificity (1-false positive rate) and 85% sensitivity. When the prediction score is calculated by using the linear regression model, corresponding chi-sq is calculated, the score corresponding to the maximum value position is set as the optimal threshold score, and the prediction threshold score is 0.5219. When the linear regression model is used for evaluation, the response is that the score is larger than the threshold score, and endocrine treatment can be established; less than the threshold score is unresponsive and is immediately considered for adjuvant chemotherapy or other adjuvant treatment based on existing endocrine therapy.

The best model was constructed with 23 genes, and for the individual genes in the model, the box plot of their expression levels in the response (1) and non-response (0) populations is shown in fig. 3, and the ROC curve for distinguishing the response degree of breast cancer patients to adjuvant endocrine therapy with these individual genes as markers is shown in fig. 4, and it can be seen from fig. 3 and 4 that most genes in the model have individual diagnostic efficacy, and the foregoing discussion of the individual genes in the model indicates that the genes screened in this example are reasonable. Although the AUC of several genes is only 0.5 or slightly more than 0.5, and has no obvious diagnostic value, the 23 gene model embodies the synergistic effect with other genes, and the diagnostic efficacy of the 23 gene model is improved.

For a plurality of genes in the model, K (2, 3 … … 22) genes are randomly selected from the model genome, the model is reconstructed according to the method and cross-validation is performed, and partial results are shown in fig. 5, and it can be seen from the figure that the reconstructed model by selecting a subset consisting of 2 or more genes in the 23 gene sets also has a better diagnosis value, but the diagnosis value increases with the increase of the gene factors, so that any number of genes in the selected 23 gene sets can be used as an index for evaluating the responsiveness of the breast cancer patient in adjuvant endocrine therapy, and the closer to all 23 genes, the higher the diagnosis accuracy may be.

Example 2

The present embodiment provides a kit for evaluating responsiveness of breast cancer patients to adjuvant endocrine therapy, the kit comprising reagents capable of quantitatively detecting mRNA levels of the following 10 genes: u2AF2, SMARCB1, GAPDH, GUSB, KIT, ESR1, WAS, EP300, TPX2 and GLUD1, and the reagents include reverse transcriptase, primers, Taq enzyme, fluorescent dye, and the like.

Example 3

The present embodiments provide an apparatus for assessing responsiveness of a breast cancer patient to supplemental endocrine therapy, the apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable by the processor. The method for assessing the responsiveness of the device to adjuvant endocrine treatment of breast cancer patients is as follows:

1. post-operative cancer tissue section samples from breast cancer patients are selected for mRNA extraction.

2. The extracted mRNA was sent to a detection device to obtain the following 23 genes: information a quantitative expression levels of U2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1_i。

3. According to a scoring formula

Substituting the expression conditions of all genes into the response score of the auxiliary endocrine therapy; and dividing the risk scores of the subjects into different risk groups according to one or more preset threshold values, considering the different risk groups to be directly applicable to endocrine therapy or other adjuvant therapies, and carrying out transcriptome diagnosis of accurate therapy of HR + HER 2-breast cancer patients.

The present application has been described in detail with reference to the embodiments, but the present application is not limited to the embodiments described above, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

Claims

1. Use of a reagent for detecting a gene for the manufacture of a product for evaluating the responsiveness of a breast cancer patient to adjuvant endocrine therapy, wherein said gene comprises: u2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer selected from 1-23.

2. The use of claim 1, wherein the agent detects the mRNA expression level of the gene.

3. The use according to claim 1, wherein the molecular typing of a breast cancer patient is HR positive HER2 negative.

4. The use of claim 1, wherein the primary tumor stage of a breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3;

preferably, the regional lymph node stage is N0.

5. A kit for evaluating responsiveness of breast cancer patient to adjuvant endocrine therapy, comprising a reagent for detecting a gene comprising: at least N of the group consisting of U2AF2, CREBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1, wherein N is any positive integer from 1 to 23;

preferably, the agent detects the mRNA expression level of the gene.

6. The kit of claim 5, wherein the breast cancer patient is molecularly typed as HR positive HER2 negative;

preferably, the primary tumor stage of the breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3.

7. A computer-readable storage medium having computer-executable instructions stored thereon for causing a computer to:

step 2: mathematically correlating said expression levels to obtain a score; the score is used to indicate responsiveness of a breast cancer patient to adjuvant endocrine therapy.

8. The computer-readable storage medium of claim 7, wherein the expression level is a transcription level of the gene.

9. The computer-readable storage medium of claim 7,

a_iis the expression level of the gene, b_iSetting weight for the gene, wherein n is the number of the gene;

preferably, when the score is higher than a set value, a breast cancer patient is indicated to have high responsiveness to endocrine treatment after surgery.

10. An electronic device, comprising a processor and a memory, the memory having stored thereon a computer program executable on the processor, the processor when executing the computer program implementing the following: