CN115029437A

CN115029437A - Application of reagent for detecting biomarkers in sample in preparation of chemotherapy response assessment product for breast cancer patient

Info

Publication number: CN115029437A
Application number: CN202210460084.1A
Authority: CN
Inventors: 饶皑炳
Original assignee: Shenzhen Luwei Biotechnology Co ltd
Current assignee: Shenzhen Luwei Biotechnology Co ltd
Priority date: 2022-04-28
Filing date: 2022-04-28
Publication date: 2022-09-09
Also published as: WO2023207000A1

Abstract

The invention discloses application of a reagent for detecting biomarkers in a sample in preparation of a chemotherapy response evaluation product for a breast cancer patient. A first aspect of the application provides the use of an agent for detecting a biomarker in a sample, the biomarker comprising several of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, scan 1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, in the manufacture of a product for assessing the chemotherapeutic response of a breast cancer patient. Establishing a chemotherapy response model aiming at a breast cancer patient, and applying the gene model to effectively reflect the chemotherapy response of the subject and guide a medical treatment scheme by acquiring the level of a related biomarker of the breast cancer patient.

Description

Application of reagent for detecting biomarkers in sample in preparation of chemotherapy response assessment product for breast cancer patient

Technical Field

The application relates to the technical field of molecular diagnosis, in particular to application of a reagent for detecting biomarkers in a sample in preparing a product for evaluating the response of breast cancer patients to chemotherapy.

Background

The treatment means of breast cancer includes surgical treatment, postoperative adjuvant treatment, preoperative neoadjuvant treatment and the like, and the adjuvant treatment includes local treatment (such as adjuvant radiotherapy) and systemic treatment (such as chemotherapy, hormone treatment and targeted treatment) and the like. Because breast cancer cells have the characteristics of invasion and distant metastasis like other cancer cells, part of patients still have recurrent metastasis after surgical resection, and postoperative adjuvant treatment is needed. In the specific selection of the auxiliary treatment means, the multiple gene detection indexes of the transcriptome are widely applied, and comprise a 70 gene detection system (Mammaprint), a 21 gene detection system (Oncotype DX), a 50 gene detection (PAM50), a Breast Cancer Index (BCI) and the like. Taking Oncotype DX as an example, the system can be used to evaluate the recurrence risk score of breast cancer patients, giving a score of 0-100. For lymph node negative HR +/HER 2-patients, when the recurrence score is <26, indicating a lower recurrence rate, adjuvant chemotherapy can provide less additional benefit to the patient and therefore is not recommended. It can be seen that such a multi-gene detection system determines the benefit of increased adjuvant chemotherapy after surgery by assessing the risk of recurrence, and there is no corresponding model to directly examine the degree of patient response to adjuvant chemotherapy, thereby assessing the sensitivity of the patient to chemotherapy. Therefore, there is a need to find a model that can effectively assess the chemotherapeutic response of breast cancer patients.

Disclosure of Invention

The present application is directed to solving at least one of the problems in the prior art. To this end, the application proposes the use of reagents for detecting biomarkers in a sample for the preparation of a product for assessing the response of a breast cancer patient to chemotherapy.

In a first aspect of the present application, there is provided a use of a reagent for detecting a biomarker in a sample for preparing a product for assessing the chemotherapeutic response of a breast cancer patient, the biomarker comprising: at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, wherein N is an optional positive integer from 1 to 20.

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

the method is characterized in that a model related to chemotherapy response is established for a breast cancer patient population, relevant biomarkers of the breast cancer patient are obtained through a detection reagent, the model is established based on the biomarkers, the model established by utilizing a plurality of biomarkers can effectively reflect the response degree of a subject to auxiliary chemotherapy, a proper treatment method is selected for different chemotherapy response degrees, and the high sensitivity of the patient with high chemotherapy response degree to the chemotherapy is reflected, so that the treatment confidence is improved; for the population with low chemotherapy response degree, other auxiliary treatment schemes can be formulated as soon as possible under the condition of needing auxiliary treatment, so that the personalized accurate medical scheme is constructed.

Among them, BRD2(Bromodomain binding 2) encodes a transcription regulatory factor belonging to the BET (Bromodomain and extra-terminal domain) protein family. During mitosis, the protein binds to the transcription complex and acetylated chromatin, and selectively binds to acetylated lysine-12 residues of histone H4 through its two bromooxysomes.

PARP8(Poly (ADP-Ribose) Polymerase Family Member 8) is a protein Poly ADP ribosylation Polymerase 8 gene, the protein encoded by which initiates protein ADP ribosylation enzyme activity, participates in the automatic ADP ribosylation of proteins and the mono ADP ribosylation of proteins, and the related pathways include metabolism and niacin metabolism.

MTUS1(Microtubule Associated Scaffold Protein 1) is a Microtubule-Associated Scaffold Protein 1, the encoded Protein of which contains a C-terminal domain capable of interacting with the AT2 receptor and a large helical region that allows dimerization. One transcript variant of this gene encodes a mitochondrial protein that acts as a tumor suppressor and is involved in the AT2 signaling pathway.

TRAF1(TNF Receptor Associated Factor 1) is the tumor necrosis Factor Receptor-related Factor 1 gene, and this protein forms a heterodimer complex with TRAF2, is necessary for TNF- α -mediated activation of MAPK8/JNK and NF- κ B, and also interacts with apoptosis-inhibiting proteins (IAPs), thereby mediating anti-apoptotic signals from TNF receptors.

ADAM11(ADAM Metallopeptidase Domain 11) is an ADAM Metallopeptidase Domain 11 that encodes a family member of proteins involved in a variety of biological processes involving cell-cell and cell-matrix interactions, including fertilization, muscle development, and neurogenesis. In addition, the gene is a candidate cancer suppressor gene representing human breast cancer.

RAX (Retina And antioxidant Neural Fold Homeobox) is a homeobox gene of retinal And Anterior nerve folds, which encodes a homeobox containing transcription factors that play a role in eye development, are necessary for retinal cell fate determination, And regulate stem cell proliferation.

DPT (Dermatopontin) is a dermatopontin, an extracellular matrix protein encoded by this gene, which may play a role in cell-matrix interactions and matrix assembly. It is present in various tissues and many tyrosine residues are sulfated. Furthermore, dermatopontin may alter the behaviour of TGF-. beta.by interacting with decorin.

Ahsp (alpha Hemoglobin Stabilizing protein) is an alpha Hemoglobin Stabilizing protein gene that encodes a chaperone that specifically binds to free alpha-globin and participates in Hemoglobin assembly. The encoded protein binds to alpha-globin until the alpha-globin forms a heterodimer with beta-globin and further forms stable tetrameric hemoglobin.

CSF3(Colony Stimulating Factor 3) is a Colony Stimulating Factor 3 gene that encodes a member of the cytokine IL-6 superfamily, the encoded cytokine controlling the production, differentiation and function of granulocytes. Granulocytes, in turn, are leukocytes that are part of the innate immune response.

CITED1(Cbp/P300 Interacting transactor With Glu/Asp Rich carboxyl-Terminal Domain1) is a Transactivator gene of Cbp/P300 Interacting With Glu/Asp-Rich Carboxy-Terminal Domain1, which is related to the Asp/Glu-Rich C-Terminal Domain protein family and may function as a transcription co-activator.

CLCA2(Chloride Channel access 2) is a Chloride Channel Accessory 2 gene that encodes a member of the calcium activated Chloride Channel regulator (CLCR) protein. Members of the family regulate chloride on plasma membranes. The encoded protein is processed from protein decompression to produce N-and C-terminal fragments. The expression of this gene is upregulated by the tumor suppressor protein P53 in response to DNA damage. In breast cancer, the expression of this gene is down-regulated and the encoded protein can inhibit migration and invasion while promoting mesenchymal-epithelial switching in cancer cell lines.

BUB1B (BUB1 Mitic Checkpoint series/Threonine Kinase B) is BUB1 with a Serine molecular Checkpoint Serine/Threonine Kinase B gene that is involved in the spindle Checkpoint function, is localized to kinetochore, and plays a role in the promotion of complex/loop (APC/C) in late inhibition, delaying the onset of late phase and ensuring proper chromosome segregation.

TSPYL5(Testis-Specific Y-Encoded-Like Protein 5) is Testis-Specific Y-Encoded-Like Protein 5 gene, and the Protein Encoded by the gene may have the effects of activating chromatin binding activity and histone binding activity, and simultaneously participates in various processes including the reaction of cells to gamma radiation, the positive regulation of Protein kinase B signals, the positive regulation of Protein ubiquitination and the Like.

ABCC3(ATP Binding Cassette subset C Member 3) is an ATP-Binding Cassette Subfamily C Member 3 gene whose encoded protein belongs to the ATP-Binding Cassette (ABC) transporter superfamily, which transports a variety of molecules both extracellularly and intracellularly. The protein is a member of MRP subfamily in the superfamily, but the specific function is not determined.

SCAND1(SCAN Domain containment 1) encodes a SCAN Domain-Containing protein. The protein includes a highly conserved leucine-rich motif of about 60 amino acids, which binds to and potentially regulates the function of the transcription factor myeloid zinc finger 1B.

Ptgfr (prostaglandin F receptor) is a prostaglandin F receptor gene which encodes a protein that is a member of the G protein-coupled receptor family. As a receptor for prostaglandin F2 α (PGF2 α), is a potent luteinizing agent and may also be involved in regulating intraocular pressure and uterine smooth muscle contraction.

ASXL1(ASXL Transcriptional Regulator 1) is an ASXL Transcriptional Regulator 1 gene that encodes a chromatin binding protein for the normal determination of the identity of fragments in developing embryos. This protein is a member of the polyclonal proteome and is essential for maintaining stable inhibition of the homeotic and other sites.

PTGER4 (prostagladin E Receptor 4) is a Prostaglandin E Receptor 4 gene that encodes a protein that is a member of the G protein-coupled Receptor family. As one of four receptors for prostaglandin E2(PGE2), this receptor can activate T-cell factor signaling.

MAP7D1(MAP7 Domain containment 1) is a MAP7 Domain1 gene, and its encoded protein is located in cytoplasm and microtubule cytoskeleton, and may be involved in microtubule cytoskeleton organization. The important homologous gene is MAP 7.

MDM4(MDM4 Regulator of P53) is the MDM4 Regulator gene of P53, which encodes a nucleoprotein structurally similar to the P53 binding protein MDM 2. These two proteins bind to and inhibit the activity of the p53 tumor suppressor protein and have been shown to be overexpressed in a variety of human cancers.

Chemotherapy response refers to the degree of sensitivity of a patient to chemotherapy treatment of breast cancer, i.e., whether benefit is obtained from treatment with a chemotherapeutic agent. The chemotherapy drugs include 5-fluorouracil (F), cyclophosphamide (C), methotrexate (M), epirubicin (E), anthracycline (A), and taxa (T). Specific treatment regimens for chemotherapeutic drugs are, for example, CMF, AC, TC, AC-T, TAC, FEC-T, FAC, and the like. Benefit is at least understood to mean complete remission, or partial remission, or stabilization of the condition by chemotherapy; a relative inability to benefit from chemotherapy means that there is still significant progress through chemotherapy.

In some embodiments of the present application, the biomarkers include 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, and all twenty thereof.

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

In some embodiments of the present application, the agent detects the protein expression level of the biomarker.

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 some embodiments of the present application, the breast cancer patient is HR + HER2-, T1-T2, N0, and the chemotherapeutic response refers to the degree of response to a single or multiple dose chemotherapeutic regimen, including AC, TC, AC-T, TAC, and the like.

In some embodiments of the present application, the sample is selected from at least one of blood, tissue, stool, urine.

In a second aspect of the application, there is provided a kit for assessing the response of a breast cancer patient to chemotherapy, the kit comprising reagents for detecting biomarkers comprising: at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, wherein N is an optional positive integer from 1 to 20.

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 biomarker in a sample from a breast cancer patient, the biomarker comprising: at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, N is optionally a positive integer from 1 to 20;

step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the chemotherapeutic response of the breast cancer patient.

And guiding the individualized precise medical treatment scheme taking the chemotherapy response as a clinical target according to the obtained chemotherapy response score, so that the clinical application complementary to the existing assessment method can be formed.

In some embodiments of the present application,

a _i is the expression level of a biomarker, b _i The weight of the biomarker is set, N is the number of the biomarkers, and N is less than or equal to N.

In some embodiments of the present application, a breast cancer patient is indicated to have a higher chemotherapy response when the score is above a set value. Wherein the set value can be at least a set threshold value based on a specific scoring formula, which can effectively distinguish patients with high chemotherapy response from patients with low chemotherapy response. For example, there is a significant difference in responsiveness to chemotherapy in both groups of patients above and below the set point above which the patient can benefit from chemotherapy to achieve complete remission, partial remission, or at least stable disease; when below the set point, the patient may have difficulty benefiting from chemotherapy and progress may occur, thus considering the choice of adjuvant treatment modalities other than chemotherapy.

In some embodiments of the present application, the score is 0.2715 × BRD2+0.1236 × PARP8+0.1185 × MTUS1+0.1124 × TRAF1+0.1039 × ADAM11+0.0906 × RAX +0.0874 × DPT +0.0718 × AHSP +0.0699 × CSF3+0.0358 × CITED1+0.0247 × CLCA2-0.055 × BUB1B-0.0719 × TSPYL5-0.0726 × ABCC3-0.0775 × scan 1-0.0978 × PTGFR-0.156 × ASXL1-0.1674 × PTGER4-0.1707 × MAP7D1-0.2713 × MDM4, the abbreviation of a biomarker in the formula indicates the expression level of the corresponding biomarker.

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 levels of biomarkers in a sample from a breast cancer patient, the biomarkers comprising: at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, N is optionally a positive integer from 1 to 20;

In some embodiments of the present application,

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs and non-transitory computer executable programs, such as the process for assessing the response to chemotherapy in a breast cancer patient as described in the embodiments of the present application. The processor implements the evaluation of the chemotherapy response of the breast cancer patient by executing a non-transitory software program and instructions stored in the 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 a computer program for executing the 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 the above described chemotherapy response of breast cancer patients is further discussed below:

the model involves 20 biomarkers, in order of weight from large to small: BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM 4. Wherein, the weights of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1 and CLCA2 are positive numbers, and the chemotherapy response is promoted; while BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4 weights are negative, attenuating chemotherapeutic response. These genes can be simply classified into:

transcription regulation: BRD2, CITED1, ASXL1, RAX, SCAND 1;

repairing DNA damage: PARP8, CLCA 2;

cell proliferation and cell cycle: BUB1B, MTUS 1;

apoptosis: TRAF1 of the TNF- α pathway;

cytoskeleton and tissue architecture: DPT, MAP7D1, ADAM 11;

immune regulation and control: CSF3, PTGER4, PTGFR;

p53 regulates: TSPYL5, MDM 4;

drug/toxin metabolism: ABCC3, AHSP.

In the chemotherapy response model described above, BRD2 was weighted most positively, indicating that high expression of BRD2 responded most to chemotherapy. BRD2 belongs to the Bromodomain and Ectodomain (BET) family of proteins. There are four BET family members: BRD2/3/4 and BRDT regulate transcription by binding to acetylated lysine on histones and recruiting general transcription factors and epigenetic regulators. BET proteins, which are involved in the interaction of metabolic inflammation with breast cancer in various ways, play a key role in inflammation and lipogenesis, co-activate almost all known NF-kB regulated target genes, and also co-activate or co-inhibit PARPr regulated target genes. Studies have shown that BET proteins regulate both PD-L1 and PD-1 expression in T cells, while structural functional studies in BRD2 have shown that BRD2/BRD4 act as histone chaperones to remodel histones during transcriptional elongation so that RNA polymerase ii (rnapii) moves along the DNA to participate in transcriptional elongation; on the other hand, BRD2/BRD4 promote mitotic memory by maintaining binding to the M/G1 gene during cell division, and recruit P-TEFb to DNA in the later phase of mitosis, thereby participating in the expression of cell cycle and cell growth genes. In addition, it has been found that BET proteins promote resistance of ER-positive breast cancers to tamoxifen. Also in the HER positive population, the combination of BRD2, BAZ1A, TRIM33 and ZMYND8 was associated with RFS, while the combination of BRD2, BAZ1A, PHIP, TRIM33, KMT2A, ASH1L and PBRM1 was associated with particularly poor OS.

The accessory transcriptional activator CITED1 that promotes the response to chemotherapy in the model is involved in many transcriptional pathways. The study finds that in the adolescent mammary gland development process, CITED1 assists ESR1 to co-regulate gene expression and co-express with ABL2(ARG) and STC 2. In breast cancer, the closer the CITED1/ESR1/ARG/STC2 signal pathway is to the physiological pathway state of normal breast during adolescent breast development, the better the corresponding clinical prognosis.

The transcription factor ASXL1 that attenuates chemotherapy response in the model is a member of polycombin (Polycomb). Survival analysis research on breast cancer shows that ASXL1 high expression corresponds to better RFS; the ASXL1 knockout can affect the expression of a plurality of genes of an EMT pathway, enhance the migration of cancer cells and induce the characteristics of stem cells; whereas knockout of PTEN (AXSL1 target gene) results in EMT; in addition, ASXL1 also regulates the expression of methylases, members of the SET1 compounds. Furthermore, studies on ubiquitin hydrolase BAP1 found that mutations resulted in inactivation of the regulation of BAP1 ubiquitin hydrolase activity by ASXL1, whereas BAP1 enhanced BRCA 1-mediated inhibition of breast cancer cell growth by binding to the ring finger domain of BRCA1, thus BAP1 mutations disabled ASXL1 by activating the cancer suppressor mechanism of BAP 1.

Another transcription factor that promotes the response to chemotherapy in the model is the retinal and anterior nerve fold homeobox gene, RAX. Homologous box genes are less studied at present, and in addition to being related to the morphogenesis of eyes, the homologous box genes in the development of the pineal gland of the rodent can also maintain the proliferative activity of retinal progenitor cells, so that mice with the RAX knockout have forebrain deficiency. In addition, the homeobox gene PRRX1 plays a role in EMT, while RAX is linked to PRRX1 in that it contains an OAR region consisting of 15 amino acids, common to OTP, ARX and RAX. However, the mechanism by which RAX promotes the response to breast cancer chemotherapy needs more research.

The transcription factor SCAND1, which negatively acted on chemotherapeutic response in the model, regulated transcriptional activity. Relevant experiments of prostate cancer cell lines show that SCAND1 down-regulates CDC37 expression, while MZF1 up-regulates CDC37 expression, so that SCAND1 has an inhibiting effect on prostate cancer cells. CDC37 and HSP90 are combined to maintain HSP90 stability, are molecular chaperones of kinases on multiple pathways of cell proliferation and transformation, and comprise SRC, RAF-ERK pathway, AKT, NF-kB inhibitor, CDK4 and the like. In HEK293T cells, however, SCAND1 also interacted with TET 2.

The DNA damage repair gene PARP8 was weighted as a large positive number in the model, indicating that PARP8 promotes chemotherapeutic response. PARP8 is mainly locally expressed at the spindle pole of nuclear membrane and cell division, PARP8 knocked-out cells show abnormal bileaflet nuclei, changes in cell morphology and diminished activity of all PARP cells. Thus, PARP8 may be involved in the assembly and maintenance of cell membranes and nuclear envelope organelles. PARP8 not only catalyzes protein poly ADP ribosylation, but also catalyzes Mono ADP ribosylation (Mono-ADP Ribose, MARylation), but there is currently no study on the biological and cellular functions of PARP 8. PARP8 shown in the model promotes the chemotherapeutic response outcome, the mechanism of which needs further exploration.

CLCA2 activated by DNA damage in the model was weighted positive and had a promoting effect on the chemotherapy response. Research shows that CLCA2 is a P53 target gene and is involved in the cancer suppressor pathway of P53, P53 binds to a promoter thereof to activate CLCA2, and knockout of CLCA2 enhances the expression and activation of FAK, so that CLCA2 suppresses the metastasis and invasion of cancer cells by weakening the FAK pathway. Furthermore, CLCA2 is a TNBC prognostic biomarker in african american women, associated with poor OS, which may indicate that CLCA2 acts differently in TNBC than its HR + HER 2-breast cancer targeted in the examples of the present application.

Genes involved in regulating and controlling the cancer suppressor gene P53 in the model comprise TSPYL5 and MDM4, and the weights are negative numbers, wherein the absolute value of MDM4 is the maximum in the negative weight, which shows that TSPYL5 and MDM4 have negative effects on chemotherapy response, and MDM4 has the maximum effect on chemotherapy resistance. The report shows that TSPYL5 inhibits the cancer suppressor gene P53 by down-regulating USP7, thereby promoting the growth of cancer. A study of 772 postmenopausal early ER + breast cancer samples that are being prepared to receive aromatase inhibitors showed that the estrogen response element created by SNP-rs2583506 of TSPYL5 increased the concentration of E2 in plasma; in addition, the high expression of TSPYL5 results in the high expression of CYP19A1, which indicates that TSPYL5 may be a transcription factor involved in regulating CYP19A1 and other genes, and CYP19A1 catalyzes C19 androgen, androstenedione and testosterone to be converted into C18 estrogen, estrone and estradiol respectively. TSPYL5 binds to CYP19A1 gene skin/adipocyte promoter I.4, and the motif of the binding target DNA is presumed to be 5 '-TCANNGAAGGCAG-3'. In addition to CYP19a1, there are approximately 40 other genes that contain this motif.

The nuclear protein MDM4 has the function of inhibiting the expression of a cancer suppressor gene P53. Among MDM family proteins, the E3 ligand MDM2 negatively regulates P53 expression, being modeled by MDM 4. When cells are stressed, DNA damage initiates P53 expression, which in turn initiates transcription of MDM2/4, which constitutes a bidirectional regulatory closed loop of MDM-P53. During breast development, MDM2 determines normal breast morphology and the formation of structural ducts, while high expression of MDM2/4 is a high risk factor for breast cancer. Related researches show that different expressions of MDM4 in breast cancer cells correspond to different chemotherapy sensitivities, and inhibition of MDM4 expression can start P53, so that the effect of chemotherapy is enhanced. This also confirms to some extent that MDM4 is the most negative factor in the response to chemotherapy in the model provided in the examples of the present application.

Genes associated with the NF-kB pathway and MAPK pathway in the model were weighted positive for TRAF1, and since TRAF1 is downstream of CD30/CD30L signaling, CD30/CD30L/TRAF1 signaling was presumed to play a positive role in chemotherapy. CD30, TNF receptor TNFRSF8, is expressed in activated T and B cells, TRAF1 forms a heterodimer complex with TRAF2, and is required for TNF-alpha mediated activation of MAPK8/JNK and NF-kappa B. The research finds that the TRAF1 expression is related to the sensitivity of tamoxifen in 44 breast cancer cell models, and the gradient experiment of comparing the TRAF1 knocked-out breast cancer cell line ZR-75-1 with the gradient experiment of normal cells to tamoxifen dosage shows that the gradient curve of the living cell rate of TRAF1 knocked-out shows a faster descending trend and is lower in each gradient, which indicates that tamoxifen is easier to kill cells without TRAF1 expression, but the correlation of TRAF1 and chemotherapy is not related to the research. In a retrospective study of 284 breast cancers, no ALK-positive or CD 30-positive examples were found by IHC staining of the pathological sections, perhaps indicating that the cancer microenvironment is deficient in immune cells.

The weight of the mitotic spindle checkpoint function kinase BUB1B in the model was negative, indicating that BUB1B promotes chemotherapeutic response. Studies of 18 adult female breast cancer patients found that TOP2A, MCM2 and BUB1B proteins are potential markers of malignancy in histologically normal and benign breast tissues. BUB1B is an essential spindle checkpoint molecule for cell damage and post-mutation mitosis, a target for synuclein gamma (SNGG), and the binding of both results in the inactivation of the mitotic checkpoint, rendering breast cancer cells resistant to microtubule inhibitors. Several studies have shown that the mRNA levels and protein levels of BUB1B are highly expressed in breast cancer. The high expression of 8 checkpoints, including BUB1B, in breast cancer is probably due to defects in other molecular components of the mitotic spindle damage checkpoint, rather than to mutations or low expression of the checkpoint itself. The five genes of the breast cancer Molecular Grading Index (MGI) are: BUB1B, CENPA, NEK2, RACGAP1 and RRM 2. Clinical comparative studies of MGI + HOXB13 IL17BR and Breast Cancer Index (BCI) showed that both correlate with risk of death from ER + lymphoblastic metastasis without adjuvant chemotherapy.

Another cell cycle associated gene that promotes chemotherapeutic response in the model is MTUS 1. MTUS1, as a cancer suppressor gene, is poorly expressed in many cancers. It encodes an ATIP family protein capable of interacting with angiotensin II receptor (AT2), including ATIP1, ATIP3, ATIP 4. ATIP3 is mainly expressed in microtubule, mitotic spindle and central body, can respectively act with EB1, KIF2A and DDA3, participate in signals such as ERK, AURKA, KIF2C, CDC25B and CDK1, has wide functions, and can regulate microtubule power, spindle size, central body number, cell proliferation, polarization, EMT and the like. The expression analysis of ATIP3 in breast cancer shows that ATIP3 expression is low in 50% of breast cancers, TNBC is 70% of breast cancers, and the low expression is related to the clinical prognosis of breast cancer malignancy, poor OS, RFS, MFS and the like. Since ATIP3 has microtubule stabilizing effect at the positive end of microtubules, low expression of ATIP3 promotes sensitivity of cancer cells to therapeutic drugs such as paclitaxel. In a clinical study data using 3 breast cancer chemotherapeutic regimens, specifically a predictive pathological complete response (pCR) 17-gene model established using only 280 microtubule regulatory genes, MTUS1 expression was abnormally low. Chemotherapeutic regimens in three clinical studies include T, AC, FAC, etc. Deletion of ATIP3 promotes mitotic abnormalities, including centrosome amplification and formation of a multipolar spindle, leading to chromosomal segregation errors and aneuploidy. Inhibition of ATIP3 expression coupled with paclitaxel treatment promoted aneuploid cell defects, leading to massive cell death. This is contrary to the model conclusions, which may be used with other chemotherapeutic agents.

Dermatopontin DPT in the model contributes to the chemotherapeutic response. DPT is expressed primarily in the extracellular matrix (ECM). DPT binds to Decorine (DCN) and competes with DCN, preventing the formation of DCN-TGF-b complex, thereby promoting TGF-b bioactivity and inhibiting cancer cell growth. In liver cancer samples, DPT promoter methylation leads to low expression of DPT and is associated with metastasis and prognosis. In addition, there is a mutual response between CAF and tumor-infiltrating immune cells.

Another gene ADAM11 related to the tissue framework in the model has a large promotion effect on the response of chemotherapy. ADAM11 belongs to a member of the disintegrin and metalloprotease family, and it was found in a retrospective study of 259 ER positive breast cancer cryopreserved specimens that these patients all had Tamoxifen as the first line drug and later had cancer recurrences. For 153 patients containing more than 30% of matrix (Stromal) cells, the high expression of ADAM11 and ADAM9 was significantly correlated with the high efficacy of Tamoxifen. In the ADAM family of proteins, ADAM11 is an integrin ligand and has the functions of neural adhesion and tumor inhibition.

The second largest negative weight absolute value for the tubulysin MAP7D1 in the model indicates a particularly large negative effect on the chemotherapeutic response. The high expression of MAP7D1 promotes cancer growth and metastasis, and is a high risk factor for breast cancer lymph node metastasis. MAP7D1 was also associated with chemotherapy side effects, doxorubicin caused increased reactive oxygen species and apoptosis of cardiomyocytes by inhibiting autolytic enzyme formation, while MAP7D1 protein further disrupted autophagosome formation, aggregated toxic proteins, exacerbated doxorubicin-induced cardiotoxicity and cardiac arrest.

The variables of the model included two prostaglandin receptors, PGE2 receptor PTGER4(EP4) and PGF2 α receptor PTGFR, both of negative weight and larger absolute value, indicating that they both have a greater negative effect on the response to chemotherapy. Thus, prostaglandin-related signals may have a very important role in the non-response to chemotherapy. As to the role and mechanism of prostaglandin in breast cancer TME immune regulation, research shows that under the action of cytosolic phospholipase A2, cell membrane phospholipid secretes Arachidonic Acid (AA), prostaglandin G/H synthase 1/2, COX-1/COX-2 converts AA into prostaglandin PGG2, and then PGG2 is reduced to PGH 2. PGH2 can be converted by various PG synthetases into PGD2, PGE2, PGF2 α, PGI2 and Thromboxane (TX) a2, and then act through different downstream G protein-coupled receptors. Under the action of prostaglandin PGE2, its receptor PTGER4 activates Adenylate Cyclase (AC) and PKA. On the other hand, it was found that DNA demethylation of the promoter region up-regulates PTGER4 expression, leading to estrogen-independent growth of cancer cells by ligand-independent activation of ESR1 co-factor CARM1 (regulated by PKA), thereby conferring resistance to endocrine therapy by Aromatase Inhibitor (AI). In TNBC cells with high expression of IRIS (IRISOE), a positive feedback loop of IL6-PGE2 between TNBC cells and Mesenchymal Stem Cells (MSC) promotes malignancy of cancer cells. The experiment of combined injection of human or mouse MSC and IRISOE shows that TNBC promotes the formation of invasive breast tumor, IL-6 and PGE2 have higher serum level and the overall survival rate is reduced. While IRIS-silenced or inactivated cells show reduced capacity for tumor formation, limited MSC entry into tumors, reduced levels of circulating IL-6 and PGE2, and prolonged OS.

Another immunomodulatory gene, CSF3, in the model is a positive factor in the response to chemotherapy. CSF3 is a member of the IL-6 superfamily that controls the production, differentiation and function of granulocytes. After 17 cases of later-stage breast cancer and cervical cancer patients are treated by using adriamycin and CSF3, the response rate reaches 80%, and the symptoms are greatly improved after two months, which shows that CSF3 can promote endocrine treatment of the breast cancer and cervical cancer patients. A serological comparison study of 136 cases of breast cancer patients before treatment and 60 cases of healthy people shows that CSF3 has no obvious difference in serum of the breast cancer patients and the serum of the healthy people, but the later period breast cancer is obviously higher than the earlier period breast cancer. In addition, studies with several TNBC samples found that CSF3 was highly expressed on TNBC, associated with CD163+ macrophages, poor OS, and significantly increased TGF- α positive cells, indicating that CSF3 promotes anti-inflammatory effects of the tumor-induced macrophage phenotype when CSF-1R is inhibited. The structure of CSF3 and the function in breast cancer are summarized in the related reviews as follows: TAM: CEACAM1 down-regulation promotes TAM to secrete CSF3, thereby promoting tumor angiogenesis and initial tumor formation; the CSF3 action on TAM enhances the secretion of TGF-alpha and promotes the migration of tumor cells; (II) MDSC: CSF3 increases Ly6G + Ly6C + granulocytes to promote the production of pro-angiogenic factor Bv8, thereby promoting breast tumor metastasis; BMP4 inhibits the expression and secretion of CSF3 by inhibiting NF-kB, resulting in the reduction of MDSC quantity and activity; in a hypoxic environment, the NF-kB signaling pathway is activated, HIF1/2 up-regulates CAIX and increases CSF3 expression, promotes mobilization of MDSC, ultimately leading to lung metastasis of breast cancer; activation of the AKT-mTOR signaling pathway increases CSF3 expression in tumor cells, promoting MDSC accumulation. Through Notch signaling, MDSCs facilitate expression of stem cell-associated genes, including Nanog, LGR5, and MSI-1, in cancer cells to promote tumor progression. Furthermore, HRAS-induced stable expression of CSF3 up-regulated MMP2 expression by activating RAC1 and promoted migration/mammary epithelial cell infiltration. Over-expression of CSF3 activates other signaling pathways including MKK3/6, p38 MAPK, ERK1/2 and AKT, thereby promoting the invasive phenotype of mammary epithelial cells; TNF-alpha promotes tumor invasion by activating the ERK2 signaling pathway to promote the expression of CSF 3.

The chemotherapy response negative gene ABCC3 in the model belongs to a member of the ATP Binding Cassette (ABC) family of drug transport. ABC family members are resistant because the extracellular drug pump ABC overexpresses, mediates drug flux out of the cell, and reduces the intracellular concentration and effectiveness of the drug.

The chemotherapy response positive gene AHSP in the model is hemoglobin a accessory protein, which binds and stabilizes hemoglobin a during normal embryonic development and hemoglobin a hyperemia, such as hemoglobin B anemia. The study finds that the AHSP promoter region contains GATA1 and OCT1/SLC22A1 binding sites, and GATA1 and OCT1 are necessary for AHSP expression. In addition to specificity for hemoglobin a, ASHP is critical for the normal formation and stabilization of hemoglobin. The gene expression molecular profile for sepsis associated with pneumonia contained 4 genes: ALAS2, AHSP, HBD and CA1, which are involved in heme/hemoglobin metabolism and protect leukocytes in malignant conditions. The mechanism by which AHSP promotes chemotherapeutic response remains to be explored.

In summary, each gene in the chemotherapy response model has been studied more or less in the field of breast cancer to show its important role in breast cancer. The group of the biomarkers screened out based on the algorithm model is verified with the research results in the field, and the reasonability of the algorithm model is shown.

It will be appreciated that the above discussion is based on a model of all 20 biomarkers, but in the present example, it is not limited to a model of all 20 biomarkers, from which selection of several biomarkers can also construct other models that result in an assessment of the effect of chemotherapy response.

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 repetitions of optimal model cross validation in an embodiment of the present application.

Fig. 2 is an ROC curve of the optimal model verified in all samples in the example of the present application.

Fig. 3 is a box plot of the expression levels of markers of a single biomarker in an optimal model in different populations in an example of the present application.

Fig. 4 is a ROC curve for markers of a single biomarker in an optimal model in an embodiment of the present application.

Fig. 5 is a cross-validation result of selecting several different biomarkers from the optimal model for re-modeling in the example of the present application, where a and b are cross-validation results of 2 biomarkers, and c and d are cross-validation results of 19 biomarkers.

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 illustrative purposes only and is not intended to be limiting of the present 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 larger, smaller, larger, etc. are understood as excluding the present numbers, and larger, smaller, inner, etc. are understood as including the present numbers. 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

1. And downloading a data set TCGA-BRCA from the gene expression comprehensive database TCGA, and downloading data sets GSE45255 and GSE69031 from GEO, wherein the data sets are gene chip data of breast cancer slices. Only early (T1-T2) breast cancer patients with ER positive or PR positive, HER2 negative, no Node 0 metastasis were selected, and had a total of 203 samples that were chemotherapy-treated and chemotherapy-responded to non-empty (Response 0 or 1). There are 167 cases of TCGA-BRCA, 23 cases of GSE45255 and 13 cases of GSE 69031. The Response rate was 25.62% for 52 cases (Response ═ 1) and 151 cases (no Response ═ 0) of the Response.

2. After gene transcription with extremely low expression is eliminated (the number of expressed nonzero samples is not more than 10), miRNA and lncRNA are eliminated, common genes of three data sets are selected, and the gene factor is 9524.

3. Data normalization was performed on each data set, and samples and genes were performed in two steps:

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

The three normalized data sets are assembled into a comprehensive data set.

Second, screening and model of gene diagnosis marker

For chemotherapy response in breast cancer patients, the model was established by:

1. genes associated with chemotherapeutic response were identified. And (3) searching genes with statistical significance (p is less than 0.05) capable of distinguishing different target variable populations (0, no response vs 1 and response) by using a t-test (t-test), and primarily 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 chemotherapy;

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

Wherein the content of the first and second substances,

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 disease is present than before chemotherapy. Tumor marker testing (as applicable) showed elevated tumor markers.

In addition, it should be noted that the chemotherapy regimens received by more than 200 patients are not identical, and statistically, the chemotherapy regimens cover up to 50 chemotherapy regimens including a single drug, sequential drug, combined drug, and the like, comprising a, C, T, F, M, AC, TC, AC-T, TAC, TAM, TAC-H, CAF, CMF, TAM-AC, TAM-CMF, and the like, and each patient receives at least one of the chemotherapy regimens, thereby showing the overall response to the chemotherapy regimens.

2. 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.

3. And (5) analyzing a hierarchical association coefficient. The method comprises the steps of respectively carrying out hierarchical association coefficient clustering on genomes with up-regulated or down-regulated expression, wherein the purpose is that genes in each cluster are required to be approximately associated with each other in pairs at a given association coefficient level, clustering is carried out through iteration, firstly, obtaining an association coefficient matrix between every two genes in the up-regulated or down-regulated genomes, and setting a first association coefficient threshold value T ₁ 0.75 (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 above 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 T ₂ ＝T ₁ -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 markersModel candidate genes.

4. Iterative linear regression analysis determined the genome. For a genome whose expression is up-regulated or down-regulated, the number(s) of genes as model parameters is predetermined in the hierarchical association coefficient analysis, and iterative linear regression analysis is performed. And recycling different s values, searching the number of the optimal model parameters, and determining the optimal model parameters according to the maximum value of the corresponding R square value (rsq).

5. Pre-selecting 741 genes on the gene mutation map related to the cancer, and repeating the step 4 to obtain an optimal model;

6. the genes of 4 and 5 were combined and repeat 4 again to give the final model.

The final chemotherapy response model was a model of 20 biomarkers: BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM 4.

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

TABLE 1.20 correlation parameters for each gene in the biomarker Linear regression model

And (3) cross validation: and (4) 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 calculation is repeated for N (20) times. 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 fig. 1, and it can be seen that the maximum value of AUC of the model after 20 times of repetition is 0.93, the minimum value is 0.66, and the median value is 0.8, which indicates that the model has good classification meaning, and can well separate people with different chemotherapy responses in breast cancer patients of HR +/HER2-, T1-T2, and N0, so that the targeted treatment scheme can be considered for different people according to different chemotherapy responses.

The ability of the 21 gene linear regression model established (score 0.2715 × BRD2+0.1236 × PARP8+0.1185 × MTUS1+0.1124 × TRAF1+0.1039 × ADAM11+0.0906 × RAX +0.0874 × DPT +0.0718 × AHSP +0.0699 × CSF3+0.0358 × CITED1+0.0247 × CLCA 1-0.055 × BUB1 1-1 × TSPYL 1-0.0726 × ABCC 1-0.0775 × scan 1-1 × PTGFR-0.156 × ASXL 1-1 × PTGER 1-1 × MAP 1-1 × MDM 1) was evaluated on early stage breast cancer patients receiving chemotherapy with the complete HR + HER 72-N1 in the data set (score 0.271203 sample Response, score 0.151, score on total Response of chemotherapy, total Response of 0.151% chemotherapy, total Response curve of total Response to total chemotherapy in patients with HR + HER 72-N1 in the data set (total score 0.2712 × ROC 2 ═ Response curve, score 0.151). The AUC was 0.9391, and the optimal decision point on the ROC curve (shown as a dashed line) corresponds to a specificity (1-false positive rate) of 89% and a sensitivity of 88%. When the linear regression model is used for calculating the prediction score, the 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 0.554. When the linear regression model is used for evaluation, a high chemotherapy response is obtained if the threshold score is larger than the threshold score, and a low chemotherapy response is obtained if the threshold score is smaller than the threshold score, so that a targeted treatment mode is considered. For patients below this threshold score, the use of adjunctive therapies other than chemotherapy is contemplated.

The best model was constructed with 20 genes, and for the individual genes in the model, the box plot of their expression levels in the population with low (0) and high (1) chemotherapy responses is shown in fig. 3, and the ROC curve for differentiating the chemotherapy responses of breast cancer patients with these individual genes as markers is shown in fig. 4, and it can be seen from fig. 3 and fig. 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 selected 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 AUC embodies the synergistic effect with other genes in the 20 gene model, and the diagnostic efficacy of the 21 gene model is improved.

For a plurality of genes in the model, K (2, 3 … … 20) genes are randomly selected from a model genome, the model is reconstructed according to the method and is subjected to cross validation, partial results are shown in Table 2 and FIG. 5, and as can be seen from Table 2 in combination with FIG. 5, the reconstructed model by selecting a subset consisting of 2 or more genes in the 21 gene sets also has better diagnostic value, and the diagnostic value is increased along with the increase of the gene factors on the whole, so that a plurality of genes selected from the selected 20 gene sets can be used as indexes for evaluating the chemotherapy response of the breast cancer patient, and the closer to all 20 genes, the higher the diagnostic accuracy is possibly.

TABLE 2 AUC values of different gene quantity construction models

Example 2

The present example provides a kit for assessing the response of a breast cancer patient to chemotherapy, the kit comprising reagents capable of quantitatively detecting mRNA levels of the following 18 genes: ABCC3, ADAM11, AHSP, ASXL1, BRD2, BUB1B, CLCA2, CSF3, MAP7D1, MDM4, MTUS1, PARP8, PTGER4, PTGFR, RAX, SCAND1, TRAF1 and TSPYL5, and the reagent comprises reverse transcriptase, primer, Taq enzyme, fluorescent dye and the like.

Example 3

The present embodiments provide an apparatus for assessing the chemotherapeutic response of a breast cancer patient, the apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable by the processor. The method of assessing the chemotherapeutic response of a breast cancer patient using the apparatus is as follows:

1. a post-operative cancer tissue section sample of a breast cancer patient is selected for mRNA extraction.

2. The extracted mRNA is sent to a detection device, and the following 19 genes are obtained: information a quantitative expression levels of ABCC3, ADAM11, AHSP, ASXL1, BRD2, BUB1B, CITED1, CSF3, DPT, MAP7D1, MDM4, MTUS1, PARP8, PTGER4, PTGFR, RAX, SCAND1, TRAF1 and TSPYL5 _i 。

3. According to a scoring formula

Substituting the expression level of each gene (n-19) to calculate a chemotherapy response score; and dividing the chemotherapy response scores of the subjects into different risk groups according to one or more preset threshold values, and considering different treatment methods for the different risk groups so as to carry out transcriptome diagnosis and treatment of HR + HER 2-breast cancer patients for accurate treatment.

Example 4

The present embodiment provides an apparatus for evaluating the chemotherapy response of breast cancer patients, which is different from embodiment 3 in that it employs a centrifugal microfluidic chip for liquid biopsy to perform detection, the centrifugal microfluidic chip is provided with at least 19 detection grooves, the detection is performed by means of liquid biopsy, a dropped blood sample enters the detection grooves by means of centrifugation through the processes of combining, washing, eluting, etc. to react with the reagents therein, and information of quantitative expression levels of these genes is obtained by fluorescence.

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 biomarker in a sample for the manufacture of a product for assessing the chemotherapeutic response of a breast cancer patient, wherein the biomarker comprises at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, scan 1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, N optionally being a positive integer from 1 to 20.

2. The use of claim 1, wherein the agent detects the level of expression of the biomarker;

preferably, the expression level is the mRNA transcript level of the biomarker.

3. The use according to claim 1, wherein the molecular typing of a breast cancer patient is 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;

preferably, the regional lymph node stage is N0.

4. The use according to any one of claims 1 to 3, wherein the sample is selected from at least one of blood, tissue, stool, urine.

5. A breast cancer patient chemotherapy response assessment product comprising reagents for detecting biomarkers comprising: at least N of BRD2, PARP8, MTUS1, TRAF1, ADAM11, RAX, DPT, AHSP, CSF3, CITED1, CLCA2, BUB1B, TSPYL5, ABCC3, SCAND1, PTGFR, ASXL1, PTGER4, MAP7D1 and MDM4, N is optionally a positive integer from 1 to 20;

preferably, the agent detects the level of expression of the biomarker;

preferably, the expression level is the mRNA transcript level of the biomarker.

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 the chemotherapeutic response of a breast cancer patient.

8. The computer-readable storage medium of claim 7, wherein the expression level is an mRNA transcription level of the biomarker.

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

a _i is the expression level of a biomarker, b _i Setting weight for the biomarkers, wherein n is the number of the biomarkers;

preferably, when the score is above a set value, a breast cancer patient is indicated to have a higher chemotherapy response.

10. An apparatus 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 performing the following: