WO2019206217A1 - 多发性骨髓瘤分子分型及应用 - Google Patents

多发性骨髓瘤分子分型及应用 Download PDF

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WO2019206217A1
WO2019206217A1 PCT/CN2019/084241 CN2019084241W WO2019206217A1 WO 2019206217 A1 WO2019206217 A1 WO 2019206217A1 CN 2019084241 W CN2019084241 W CN 2019084241W WO 2019206217 A1 WO2019206217 A1 WO 2019206217A1
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multiple myeloma
tested
mcl1
bortezomib
genes
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PCT/CN2019/084241
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English (en)
French (fr)
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樊小龙
萨莫•阿亚兹•阿里
李玖一
卢绪章
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北京师范大学
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Priority claimed from CN201810399756.6A external-priority patent/CN108559778B/zh
Priority claimed from CN201810401708.6A external-priority patent/CN108570501B/zh
Application filed by 北京师范大学 filed Critical 北京师范大学
Priority to US17/049,667 priority Critical patent/US20210055301A1/en
Priority to JP2020560444A priority patent/JP2021521857A/ja
Publication of WO2019206217A1 publication Critical patent/WO2019206217A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5011—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing antineoplastic activity
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • G—PHYSICS
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    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the invention belongs to the field of biotechnology, and in particular relates to molecular typing and application of multiple myeloma.
  • Multiple Myeloma is a tumor caused by malignant proliferation of plasma cells and is the second most common blood tumor.
  • the incidence rate in China is 1-2/100,000.
  • Multiple myeloma is a common occurrence in the elderly population older than 60 years old. As the degree of aging in China increases, its incidence has increased year by year, and it has become a serious threat to the health of the elderly.
  • a typical feature of multiple myeloma is the presence of a large number of abnormally proliferating plasma cells in the bone marrow. This plasma cell secretes an abnormal immunoglobulin or immunoglobulin fragment, M protein. The concentration of M protein is the diagnosis of multiple bone marrow. An important indicator of tumors.
  • prognostic-related gene expression features such as UAMS-70 and UAMS-17, UAMS-80, IFM-15, Millennium-100, EMC-92, gene amplification indices such as GPI-5, MRC-IX-6, and centers
  • gene amplification indices such as GPI-5, MRC-IX-6, and centers
  • the invention provides an application for obtaining or detecting a substance of 97 gene expression in a patient with multiple myeloma tumors to be tested for preparing a product for predicting the prognosis of a patient with multiple myeloma tumors to be tested.
  • the prognosis survival is reflected in the prognosis survival rate, the length of survival or the risk of survival.
  • the prognosis survival rate is overall survival or progression free survival.
  • the invention also provides the use of a substance for obtaining or detecting 97 gene expression in a patient with multiple myeloma tumors to be tested for preparing at least one functional product having the following a-c:
  • Another object of the present invention is to provide a use of a substance for obtaining or detecting 97 gene expression in a patient with multiple myeloma tumors to be tested and a device for operating a multiple myeloma Bayesian classifier.
  • the invention provides a product for obtaining or detecting 97 gene expression substances in a patient with multiple myeloma tumors to be tested and a device for operating a multiple myeloma Bayesian classifier in preparing a prognosis for predicting a patient with multiple myeloma tumors to be tested.
  • the prognosis survival is reflected in the prognosis survival rate, the length of survival or the risk of survival.
  • the present invention also provides an apparatus for obtaining or detecting 97 gene expression substances in a patient with multiple myeloma tumors to be tested and an apparatus for operating a multiple myeloma Bayesian classifier for preparing at least one functional product having the following ac :
  • the above 97 genes are as follows: ACBD3, ADAR, ADSS, ALDH2, ANP32E, ANXA2, ATF3, ATP8B2, CACYBP, CAPN2, CCND1, CCT3, CDC42SE1, CERS2, CHSY3, CLIC1, CLMN, COPA, CSNK1G3, DAP3, DENND1B, ENSA , EPRS, EPSTI1, EVL, FAM13A, FAM49A, FLAD1, FRZB, GLRX2, HAX1, HDGF, HLA-A, HLA-B, HLA-C, HLA-F, HLA-G, IL6R, ISG20L2, JTB, KLF2, LAMTOR2 , LDHA, MCL1, MOXD1, MRPL24, MRPL9, MVP, MYL6, NDUFS2, NOP58, NOTCH2NL, NTAN1, PAK1, PI4KB, PIEZO1, PIK3AP1, PIM2, PIP5K1B, PMV
  • the above multiple myeloma Bayesian classifier is obtained by a method comprising the following steps:
  • the expression data of 97 genes of n multiple myeloma samples were derived from the existing database or the expression data of 97 genes of multiple myeloma samples with more than 100 samples.
  • n is greater than or equal to 100
  • the expression levels of 97 genes were the expression levels of 97 genes in multiple myeloma cells;
  • step 3 Based on the two subtypes of step 2, the expression data of 97 genes of n multiple myeloma samples of step 1), and the prognostic survival data of n multiple myeloma samples, using the naive Bayesian method Constructed to get the Bayesian classifier.
  • the above step 3) is to randomly divide the n multiple myeloma samples into a training set and a verification set according to a ratio of a sample size ratio greater than 1:1; and then use the expression data of the 97 genes in the training, and Combining the MCL1-M-High and MCL1-M-Low subtype tags of each sample obtained by the Consensus Clustering clustering algorithm, using the naive Bayesian algorithm in the R language machine learning package klaR package to predict and predict a single patient MCL1-M Multiple myeloma Bayesian classifiers of the -High subtype and the MCL1-M-Low subtype;
  • the above-mentioned manner of obtaining the expression data of 97 genes of each multiple myeloma sample is to detect the expression level of 97 genes of the multiple myeloma sample or to obtain the expression of 97 genes of the multiple myeloma sample from the database. the amount.
  • a third object of the invention is to provide a product.
  • the product provided by the present invention comprises a substance for obtaining or detecting 97 genes in a patient with multiple myeloma tumors to be tested and a device for operating a multiple myeloma Bayesian classifier (the device may be an optical disk or a computer, etc.).
  • the product has at least one of the following functions:
  • the product has at least one of the following 1)-4):
  • the above products also include a carrier for describing the detection method
  • the detecting method comprises the following steps: the detecting method comprises the steps of: obtaining 97 patients with multiple myeloma tumors to be tested by using the substance for obtaining or detecting 97 genes in a patient with multiple myeloma tumors to be tested. Gene expression data; further, the expression data of 97 genes of the multiple myeloma tumor patients to be tested are classified by the multiple myeloma Bayesian classifier, belonging to the MCL1-M-High subtype The prognostic prognosis of patients with multiple myeloma tumors is significantly worse than that of patients with multiple myeloma tumors that are MCL1-M-Low subtypes;
  • the detecting method comprises the steps of: obtaining, by the obtaining or detecting a substance expressing 97 genes in a patient with multiple myeloma tumors to be tested, obtaining expression data of 97 genes of the multiple myeloma tumor patient to be tested; Further, the expression data of 97 genes of the multiple myeloma tumor patients to be tested are classified by the multiple myeloma Bayesian classifier, and the MCL1-M-High subtype is to be tested for multiple myeloma tumors.
  • the patient's treatment with bortezomib or bortezomib is better than the multiple myeloma tumor patients to be tested belonging to the MCL1-M-Low subtype;
  • the detecting method comprises the steps of: obtaining, by the obtaining or detecting a substance expressing 97 genes in a patient with multiple myeloma tumors to be tested, obtaining expression data of 97 genes of the multiple myeloma tumor patient to be tested; Further, the expression data of 97 genes of the multiple myeloma tumor patient to be tested are classified by the multiple myeloma Bayesian classifier, if the patient with multiple myeloma tumors to be tested belongs to MCL1-M-High Asia The type is treated with bortezomib or a drug containing bortezomib; if the patient with multiple myeloma to be tested belongs to the MCL1-M-Low subtype, then bortezomib or a drug containing bortezomib is not used.
  • the patient with multiple myeloma tumors to be tested is a single patient or a plurality of patients.
  • the n multiple myeloma samples were 551 samples.
  • the ratio greater than 1:1 is to randomly divide the training set and the verification set according to a ratio of 2:1.
  • a fourth object of the present invention is to provide a method of constructing a model for typing a patient with multiple myeloma tumors.
  • the method provided by the invention comprises the following steps:
  • the expression data of 97 genes of n multiple myeloma samples were derived from the existing database or the expression data of 97 genes of multiple myeloma samples with more than 100 samples.
  • n is greater than or equal to 100
  • the expression levels of 97 genes were the expression levels of 97 genes in multiple myeloma cells;
  • step 3 Based on the two subtypes of step 2), the expression data of 97 genes of n multiple myeloma samples of step 1), and the prognostic survival data of n multiple myeloma samples, using naive Bayes The method constructs the Bayesian classifier, which is the target model.
  • 97 gene expressions in patients with multiple myeloma tumors were derived from 97 gene expression in tumor cells of patients with multiple myeloma tumors.
  • the above-mentioned method for obtaining or detecting 97 gene expression substances in a patient with multiple myeloma tumors to be tested and/or the device for operating the multiple myeloma Bayesian classifier or the above method is prepared for predicting multiple bone marrow to be tested
  • the use of products in the prognosis survival rate of tumor tumor patients is also within the scope of protection of the present invention.
  • the above-mentioned method for obtaining or detecting 97 gene expression substances in a patient with multiple myeloma tumors to be tested and/or the device for operating the multiple myeloma Bayesian classifier or the above method is used to prepare a tumor for predicting multiple myeloma
  • the use of a patient's prognostic survival product is also within the scope of the invention.
  • the above-mentioned method for obtaining or detecting 97 gene expression substances in a patient with multiple myeloma tumors to be tested and/or the device for operating the multiple myeloma Bayesian classifier or the above method is used to prepare a tumor for predicting multiple myeloma
  • the use of a patient's prognosis survival risk product is also within the scope of the invention.
  • the invention also provides a method for typing a patient with multiple myeloma tumors, comprising the following steps:
  • the device is typed to obtain whether the patient with multiple myeloma tumors to be tested belongs to the MCL1-M-High subtype or the MCL1-M-Low subtype.
  • the present invention also provides a method for predicting the prognosis of a patient with multiple myeloma tumors, comprising the steps of: detecting or obtaining the expression data of 97 genes of the multiple myeloma tumor patient to be tested;
  • the expression data of 97 genes in patients with multiple myeloma tumors were classified by the above-mentioned multiple myeloma Bayesian classifier, and the prognosis of patients with multiple myeloma tumors belonging to MCL1-M-High subtype was significantly worse. Or inferior to patients with multiple myeloma tumors to be tested belonging to the MCL1-M-Low subtype.
  • the prognosis is reflected in the prognosis survival rate, the length of survival or the risk of survival;
  • the predicted prognosis survival rate of the multiple myeloma tumor patients to be tested belonging to the MCL1-M-High subtype is significantly inferior to that of the multiple myeloma tumor patients to be tested belonging to the MCL1-M-Low subtype as follows: 1)-3 At least one of:
  • the invention also provides a medicament for detecting the treatment effect of bortezomib or bortezomib in a patient with multiple myeloma tumors to be tested, comprising the following steps: obtaining the expression of 97 genes of the patient with multiple myeloma tumors to be tested.
  • the data of the 97 genes of the multiple myeloma tumor patients to be tested are classified by the multiple myeloma Bayesian classifier, and belong to the MCL1-M-High subtype to be tested for multiple
  • the treatment of bortezomib or bortezomib in patients with myeloma tumors is better than that of patients with multiple myeloma tumors belonging to the MCL1-M-Low subtype.
  • the invention also provides a drug for guiding bortezomib or bortezomib in a patient with multiple myeloma tumors to be tested, comprising the following steps: obtaining the expression data of 97 genes of the multiple myeloma tumor patients to be tested Further, the expression data of 97 genes of the multiple myeloma tumor patients to be tested are classified by the multiple myeloma Bayesian classifier, and the multiple bone marrow to be tested belonging to the MCL1-M-High subtype is classified. Tumor tumor patients are treated with bortezomib or a drug containing bortezomib.
  • the above-mentioned data of the expression of 97 genes of the multiple myeloma tumor patients to be tested can be obtained from the database, or the expression levels of 97 genes in the sample can be directly detected.
  • the expression levels of the above genes are all gene expression levels in multiple myeloma tumor cells.
  • Figure 1 shows the ROC diagram of the GSE2658 verification centralized Bayesian classifier.
  • Figure 2 is a ROC diagram of the Bayesian classifier classification results in the MMRF dataset.
  • Figure 3 is a ROC diagram of the Bayesian classifier classification results in the GSE19784 data set.
  • Figure 4 shows the overall survival curves of MCL1-M-High and MCL1-M-Low subtypes of multiple myeloma in GSE2658.
  • Figure 5 is the overall survival curve of MCL1-M-High and MCL1-M-Low molecular subtypes of multiple myeloma in GSE2658.
  • Figure 6 shows the overall survival curves (top panel) and progression-free survival curves (lower panel) of multiple myeloma MCL1-M-High and MCL1-M-Low subtypes in GSE19784.
  • Figure 7 shows that the MCL1-M-High and MCL1-M-Low subtypes of GS19784 have different responses to bortezomib treatment.
  • Example 1 Screening of molecular diagnostic markers for multiple myeloma and implementation of molecular typing
  • the names of 97 genes are as follows: ACBD3, ADAR, ADSS, ALDH2, ANP32E, ANXA2, ATF3, ATP8B2, CACYBP, CAPN2, CCND1, CCT3, CDC42SE1, CERS2, CHSY3, CLIC1, CLMN, COPA, CSNK1G3, DAP3, DENND1B, ENSA , EPRS, EPSTI1, EVL, FAM13A, FAM49A, FLAD1, FRZB, GLRX2, HAX1, HDGF, HLA-A, HLA-B, HLA-C, HLA-F, HLA-G, IL6R, ISG20L2, JTB, KLF2, LAMTOR2 , LDHA, MCL1, MOXD1, MRPL24, MRPL9, MVP, MYL6, NDUFS2, NOP58, NOTCH2NL, NTAN1, PAK1, PI4KB, PIEZO1, PIK3AP1, PIM2, PIP5K1B, PM
  • the 551 patients with multiple myeloma were first divided into MCL1-M- using the Consensus Clustering clustering algorithm in an unsupervised clustering manner. High and MCL1-M-Low two subtypes.
  • cluster-based classification methods cannot perform molecular typing on individual samples.
  • the 551 samples were randomly divided into a training set (369 cases) and a validation set (182 cases) in a 2:1 ratio for establishing and evaluating individualized classifiers. The sampling was carried out in a stratified sampling manner to ensure that the ratios of the MCL1-M-High and MCL1-M-Low subtypes in the training set and the test set were consistent with the original.
  • the machine learning package klaR package provided by R language is provided.
  • the naive Bayesian algorithm establishes a multiple myeloma Bayesian classifier that predicts individual patient MCL1-M-High subtypes and MCL1-M-Low subtypes.
  • the multiple myeloma Bayesian classifier code is as follows:
  • the accuracy of the classification was evaluated using 182 samples from the validation set.
  • the model is continually iteratively optimized, and the accuracy of the classification is more than 95%.
  • the accuracy data of the classifier is shown in Table 1, and the receiver working curve (ROC) is shown in Figure 1.
  • the MMRF data set differs from GSE2658 in that the amount of gene expression is obtained by RNA-seq, not the chip.
  • the predicted results are shown in Table 2, and the ROC chart is shown in Figure 2.
  • GSE19784 is also a data set for the expression of multiple myeloma. Like GSE2618, the U133 2.0 Plus chip is used to measure gene expression. However, the two are tested at different times by different experiments, and the experimental conditions may be different, which results in significant differences in the distribution and noise levels of the data.
  • the subtype prediction results of the database are shown in Table 3, and the ROC curve is shown in Fig. 3.
  • the 551 samples were classified using the multiple myeloma Bayesian classifier obtained in Example 1, and 249 were obtained.
  • MCL1-M-High subtype multiple myeloma and 302 cases of MCL1-M-Low subtype multiple myeloma were obtained.
  • Bayesian classifier to classify 97 genes of MCL1 gene group can be used to predict the prognosis of patients to be tested.
  • the expression data of 97 gene expression genes of the MCL1 gene group were respectively used in the multiple myeloma Bayesian classifier obtained in Example 1 to sample 534 samples. Divided into MCL1-M-High (231 cases) and MCL1-M-Low two subtypes (303 cases).
  • the multiple myeloma Bayesian classifier obtained in Example 1 was used to verify the expression data of the 97 genes of the MCL1 gene group of 304 samples of multiple myeloma patients (pre-treatment detection) in the database GSE19784. 304 samples were divided into MCL1-M-High (107 cases) and MCL1-M-Low subtypes (196 cases).
  • Example 3 molecular diagnostic markers and classification of multiple myeloma in predicting whether the patient to be tested can be treated with bortezomib
  • the GSE19784 multiple myeloma expression data set was obtained from a phase III drug clinical trial (HOVON-65/GMMG-HD4) with a patient's medication regimen.
  • the trial randomized patients to two groups of VAD (155 cases) and PAD (148 cases). The difference between the two was that the PAD program had more bortezomib (trade name: Wanhao).
  • the gene expression data of the enrolled patients were collected before treatment.
  • the sample was divided into two subtypes, MCL1-M-High and MCL1-M-Low, and then in MCL1-M-High (51 cases of PAD, VAD).
  • Survival analysis KM analysis and cox regression analysis was performed in 56 subtypes of MCL1-M-Low (104 cases of PAD and 92 cases of VAD) by drug treatment.
  • A is the overall survival rate of the MCL1-M-High group
  • B is the overall survival rate of the MCL1-M-Low group
  • C is the progression-free survival rate of the MCL1-M-High group
  • D is the MCL1.
  • the molecular typing of the present invention can guide the clinical use of drugs, and can avoid the use of bortezomib in the MCL1-M-Low group, on the one hand, the economic burden of the patient can be reduced, and on the other hand, the patient can bear less. Side effects caused by medical treatment.
  • Example 4 Bayesian classifier for multiple myeloma in predicting patient risk stratification
  • Bone marrow samples from 30 newly diagnosed multiple myeloma patients were newly collected.
  • CD138-positive multiple myeloma cells were purified from bone marrow using magnetic beads attached to CD138 antibody, and total RNA of multiple myeloma cells was extracted.
  • the expression of the above 97 multiple myeloma typing genes was detected by hybridization with these RNA samples using the Aung Ng PrimeView chip.
  • the inventors explored whether the gene co-expression network surrounding the key signaling pathways conserved during the development of germinal center (GC) plasma cells is Can help to elucidate the pathogenesis of MM and apply to the molecular typing of MM.
  • the inventors focused on finding a genetic network that controls the dysregulation of B cells into plasma cells in multiple myeloma because it may play a key role in the formation of multiple myeloma.
  • MCL1-M a gene module co-expressed with MCL1 gene
  • MCL-M-High subtype MCL-M-High subtype
  • MCL. -M-Low subtype MCL-M-Low subtype

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Abstract

本发明公开了多发性骨髓瘤分子型及其应用。具体地，公开了一种产品，包括获得或检测待测多发性骨髓瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备。使用上述产品，本发明鉴定出一个与MCL1基因共表达的基因模块，进而区分具有不同预后和硼替佐米敏感度的多发性骨髓瘤分子亚型。

Description

多发性骨髓瘤分子分型及应用 技术领域
本发明属于生物技术领域，尤其涉及一种多发性骨髓瘤分子分型及应用。
背景技术
多发性骨髓瘤(Multiple Myeloma，MM)是一种由浆细胞恶性增殖所导致的肿瘤，是第二常见的血液肿瘤，在中国的发病率为1-2/十万人。多发性骨髓瘤是好发于年龄大于60岁的老年人群中，随着我国老龄化程度的加重，其发病率逐年提升，已成为严重威胁老年人健康的一种疾病。多发性骨髓瘤的典型特征为骨髓中存在大量异常增生的浆细胞，这种浆细胞会分泌一种异常的免疫球蛋白或免疫球蛋白片段，即M蛋白，M蛋白的浓度是诊断多发性骨髓瘤的重要指标。
随着蛋白酶体抑制剂如硼替佐米和免疫调节药物如来那度胺、沙利度胺等的应用，多发性骨髓瘤的生存情况有了明显的改善。但是，多发性骨髓瘤目前仍然无法被完全治愈。多发性骨髓瘤在生物学上和临床上具有高度的异质性，因此，其对多药物联合治疗的反应及治疗后生存情况在不同的病人中具有巨大的差异。造成这种差异的生物学机制目前尚未被充分理解，在一定程度上阻碍了个性化精准治疗的进行。因此，为了加深对多发性骨髓瘤生物学本质的理解，辅助临床治疗决策，开发简单可靠的分子分型系统迫在眉睫。目前，国际上已有数个多发性骨髓瘤分子分型系统被提出。例如，Bergsagel等人鉴定了8种具有不同的细胞周期蛋白D(Cyclin D)表达和染色体易位的多发性骨髓瘤亚型。使用无偏无假设的转录组分析，Zhan和Broyl等人提出多发性骨髓瘤具有7-10个分子亚型，根据病人生存期的长短,这些亚型可以被进一步简化为高风险组和低风险组。此外，与预后相关的基因表达特征如UAMS-70和UAMS-17，UAMS-80，IFM-15，Millennium-100，EMC-92，基因扩增指数如GPI-5，MRC-IX-6以及中心体扩增指数也被提出。
但以上分子分型和表达特征并不能预测药物治疗反应，也不能与浆细胞的发育过程相关联，而且用于分子分型的基因与多发性骨髓瘤病因之间的关联也未被阐明。
发明公开
为了更好的揭示多发性骨髓瘤的细胞学起源，对多发性骨髓瘤进行针对性的治疗，本发明提供了如下技术方案：
本发明一个目的是提供获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质的用途。
本发明提供了获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质在制备预测待测多发性骨髓瘤肿瘤患者预后情况的产品中的应用。
所述预后生存情况体现在预后生存率大小、生存期长短或存活风险程度。
所述预后生存率为总体生存率或无进展生存率。
本发明还提供了获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质在制备具有如下a-c中至少一种功能产品中的应用：
a、检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
b、检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物的敏感度；
c、指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药。
本发明另一个目的是提供获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备的用途。
本发明提供了获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备在制备预测待测多发性骨髓瘤肿瘤患者预后情况的产品中的应用。
所述预后生存情况体现在预后生存率大小、生存期长短或存活风险程度。
本发明还提供了获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备在制备具有如下a-c中至少一种功能产品中的应用：
a、检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药 物治疗效果；
b、检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物的敏感度；
c、指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药。
上述97个基因为如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36和ZNF593。
上述多发性骨髓瘤贝叶斯分类器按照包括如下步骤的方法获得：
1)分别获得n个多发性骨髓瘤样本的97个基因的表达量数据；
n个多发性骨髓瘤样本的97个基因的表达量数据来源于现有数据库或构建样本数量大于100例的多发性骨髓瘤样本的97个基因的表达量数据；
n大于等于100；
97个基因的表达量均为多发性骨髓瘤细胞中97个基因的表达量；
2)将所述n个多发性骨髓瘤样本的97个基因的表达量数据用Consensus Clustering聚类算法分为MCL1-M-High和MCL1-M-Low两个亚型；
3)基于步骤2的两个亚型、步骤1)的n个多发性骨髓瘤样本的97个基因的表达量数据、n个多发性骨髓瘤样本的预后生存期数据，用朴素贝叶斯方法构建得到贝叶斯分类器。
上述步骤3)为先将所述n个多发性骨髓瘤样本按照样本数量比大于1:1的比例随机划分训练集和验证集；再使用训练中的所述97个基因的表达量数据，并结合所述用Consensus Clustering聚类算法获得的各样本 MCL1-M-High和MCL1-M-Low亚型标签，使用R语言机器学习包klaR包中的朴素贝叶斯算法建立预测单个患者MCL1-M-High亚型和MCL1-M-Low亚型的多发性骨髓瘤贝叶斯分类器；
上述所述获得各个多发性骨髓瘤样本的97个基因的表达量数据的方式为检测多发性骨髓瘤样本的97个基因的表达量或者从数据库中获得多发性骨髓瘤样本的97个基因的表达量。
本发明第3个目的是提供一种产品。
本发明提供的产品，包括获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备(该设备可以是光盘或者电脑等)。
上述产品中，所述产品具有如下至少一种功能：
所述产品具有如下1)-4)中至少一种功能：
1)预测待测多发性骨髓瘤肿瘤患者预后情况；
2)检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物敏感情况；
3)检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
4)指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药。
上述产品还包括记载检测方法的载体；
所述检测方法包括如下步骤：所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后情况显著差于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
或，所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于 MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物治疗效果好于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
或，所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，若待测多发性骨髓瘤肿瘤患者属于MCL1-M-High亚型，则用硼替佐米或含有硼替佐米的药物治疗；若待测多发性骨髓瘤肿瘤患者属于MCL1-M-Low亚型，则不用硼替佐米或含有硼替佐米的药物治疗。
上述产品中，所述待测多发性骨髓瘤肿瘤患者为单个患者或多个患者。
上述产品中，所述n个多发性骨髓瘤样本为551例样本。
或所述大于1:1的比例为按照2：1的比例随机划分训练集和验证集。
本发明第4个目的是提供构建对多发性骨髓瘤肿瘤患者进行分型的模型的方法。
本发明提供的方法包括如下步骤：
1)获得n个多发性骨髓瘤样本的97个基因的表达量数据；
n个多发性骨髓瘤样本的97个基因的表达量数据来源于现有数据库或构建样本数量大于100例的多发性骨髓瘤样本的97个基因的表达量数据；
n大于等于100；
97个基因的表达量均为多发性骨髓瘤细胞中97个基因的表达量；
2)将所述n个多发性骨髓瘤样本的97个基因的表达量数据用Consensus Clustering聚类算法分为MCL1-M-High和MCL1-M-Low两个亚型；
3)基于步骤2)的两个亚型、步骤1)的n个多发性骨髓瘤样本的97个基因的表达量数据、n个多发性骨髓瘤样本的预后生存期数据，用朴素贝叶斯方法构建得到贝叶斯分类器，即为目的模型。
多发性骨髓瘤肿瘤患者中97个基因表达均来自多发性骨髓瘤肿瘤患者肿瘤细胞中97个基因表达。
上述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质 和/或所述运行多发性骨髓瘤贝叶斯分类器的设备或上述方法得到的模型在制备预测待测多发性骨髓瘤肿瘤患者预后生存率的产品中的应用也是本发明保护的范围。
上述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和/或所述运行多发性骨髓瘤贝叶斯分类器的设备或上述方法得到的模型在制备预测多发性骨髓瘤肿瘤患者预后生存期产品中的应用也是本发明保护的范围。
上述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和/或所述运行多发性骨髓瘤贝叶斯分类器的设备或上述方法得到的模型在制备预测多发性骨髓瘤肿瘤患者预后存活风险产品中的应用也是本发明保护的范围。
本发明还提供了一种对待测多发性骨髓瘤肿瘤患者分型的方法，包括如下步骤：
检测或获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用上述多发性骨髓瘤贝叶斯分类器进行分型，得到待测多发性骨髓瘤肿瘤患者是属于MCL1-M-High亚型还是MCL1-M-Low亚型。
本发明还提供了一种预测多发性骨髓瘤肿瘤患者预后情况的方法，包括如下步骤：检测或获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用上述多发性骨髓瘤贝叶斯分类器进行分型，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后情况显著劣于或劣于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
所述预后情况体现在预后生存率大小、生存期长短或存活风险程度；
所述属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后生存率显著劣于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者体现为如下1)-3)中至少一种：
1)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后生存率显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
2)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预后生存期 显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
3)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者存活风险程度显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
本发明还提供了一种检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果，包括如下步骤：获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物治疗效果好于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
本发明还提供了一种指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药，包括如下步骤：获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，对属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者采用硼替佐米或含有硼替佐米的药物治疗。
上述获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据可以从数据库中获得，也可以直接检测样本中97个基因的表达量获得。
上述基因的表达量均为多发性骨髓瘤肿瘤细胞中的基因表达量。
附图说明
图1为GSE2658验证集中贝叶斯分类器分型结果ROC图。
图2为MMRF数据集中贝叶斯分类器分型结果ROC图。
图3为GSE19784数据集中贝叶斯分类器分型结果ROC图。
图4为GSE2658中多发性骨髓瘤MCL1-M-High和MCL1-M-Low分子亚型的总体生存曲线。
图5为GSE2658中多发性骨髓瘤MCL1-M-High和MCL1-M-Low分子亚型的总体生存曲线。
图6为GSE19784中多发性骨髓瘤MCL1-M-High和MCL1-M-Low分子亚型的总体生存曲线(上图)和无进展生存曲线(下图)。
图7为GS19784中MCL1-M-High和MCL1-M-Low亚型病人对硼替佐米 治疗具有不同的反应。
实施发明的最佳方式
下述实施例中所使用的实验方法如无特殊说明，均为常规方法。
下述实施例中所用的材料、试剂等，如无特殊说明，均可从商业途径得到。
实施例1、多发性骨髓瘤的分子诊断标志物的筛选及分子分型的实施
利用NCBI GEO公共数据库提供的多发性骨髓瘤表达量数据集GSE2658，通过皮尔森相关性分析，获得了87个与MCL1共表达的基因，并以此为基础，鉴定出了46个在低表达MCL1-M基因的多发性骨髓瘤样本中富集的基因。为了更稳定的进行分子分型，这133个基因中36个分类效力不高的被进一步筛除，最终97个稳定差异表达且丰度相对较高的分类基因被保留。
97个基因的名称如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36和ZNF593。
这97个基因随后将作为用于分型的分类因子。利用GSE2658数据集的551例多发性骨髓瘤样本中这97个基因的表达量数据，首先使用Consensus Clustering聚类算法以无监督的聚类方式把这551例多发性骨髓瘤分为了MCL1-M-High和MCL1-M-Low两个亚型。但基于聚类的分类方法不能针对单独样本进行分子分型。为了实施个体化诊断，这551例样本按照2：1的比例被随机划分训练集(369例)和验证集(182例)，用于建立和评估个体化的分类器。取样采取了分层取样的方式，以保证训练集 和测试集中MCL1-M-High和MCL1-M-Low两个亚型的比例与原来维持一致。
根据训练集中369例样本的97个分类基因的表达量数据和Consensus Clustering聚类算法分成的MCL1-M-High和MCL1-M-Low两个亚型标签，使用R语言中机器学习包klaR包提供的朴素贝叶斯算法建立了可预测单个患者MCL1-M-High亚型和MCL1-M-Low亚型的多发性骨髓瘤贝叶斯分类器。
多发性骨髓瘤贝叶斯分类器代码如下：
Figure PCTCN2019084241-appb-000001
Figure PCTCN2019084241-appb-000002
Figure PCTCN2019084241-appb-000003
Figure PCTCN2019084241-appb-000004
并利用验证集中182例样本评估了其分类的准确度。
根据返回的准确度，不断迭代优化模型，最终使分类的准确度超过了95％，该分类器的准确度数据见表1，受试者工作曲线(ROC)见图1。
表1.利用GSE2658数据集建立的分类器在验证集中的准确度
Figure PCTCN2019084241-appb-000005
为了确定利用GSE2658数据集建立的分类器能够被推广应用。发明人随后利用该分类器预测了NCI发布的多发性骨髓瘤大型数据集MMRF及GEO多发性骨髓瘤表达量数据集GSE19784中样本的分子亚型。
MMRF数据集不同于GSE2658，其基因的表达量通过RNA-seq获得，而非芯片。其预测的结果如表2，ROC图见图2。
表2.利用GSE2658数据集建立的分类器的在MMRF数据集中的准确度
Figure PCTCN2019084241-appb-000006
Figure PCTCN2019084241-appb-000007
结果显示，即使跨平台，该分类器也能保持很高的准确度，这说明它具有较高的推广应用价值。
GSE19784也是一个多发性骨髓瘤的表达量数据集，与GSE2618一样，采用的是U133 2.0 Plus芯片测量基因表达量。但是，两者来由不同的实验在不同的时间进行检测，实验条件可能具有差别，这导致两者的数据在分布和噪音水平上显著不同。该数据库亚型预测结果见表3，ROC曲线见图3。
表3.利用GSE2658数据集建立的分类器的在GSE19784数据集中的准确度
Figure PCTCN2019084241-appb-000008
结果显示，该分类器能较好的克服上述问题，依然保持较高的准确度。
实施例2、多发性骨髓瘤的贝叶斯分类器在预测患者预后存活率中的应用
一、数据库GSE2658
根据GSE2658数据库551例多发性骨髓瘤患者样本(治疗前检测)的97个分类基因的表达量数据，采用实施例1获得的多发性骨髓瘤贝叶斯分类器将该551例样本分类，得到249例MCL1-M-High亚型多发性骨髓瘤和 302例MCL1-M-Low亚型多发性骨髓瘤。
跟踪随访551例样本患者治疗后72个月，根据随访结果，进行生存分析(K-M分析及cox回归分析)，结果如图4所示，可以看到,MCL1-M-High和MCL1-M-Low两个多发性骨髓瘤亚型具有显著不同的预后,MCL1-M-High亚型的总体生存率比MCL1-M-Low亚型要低(log-rank检验，p＝0.0201，似然比检验，风险比1.588，p＝0.0212)。
因此，采用贝叶斯分类器利用MCL1基因群97个基因进行分型，可以用来预测待测患者的预后。
二、数据库MMRF
根据MMRF数据集中534例多发性骨髓瘤患者样本(治疗前检测)MCL1基因群97个分类基因的表达量数据，分别采用实施例1获得的多发性骨髓瘤贝叶斯分类器将该534例样本分为MCL1-M-High(231例)和MCL1-M-Low两个亚型(303例)。
跟踪随访534例样本患者治疗后48个月，根据随访结果，进行生存分析(K-M分析及cox回归分析)，结果如图5所示，可以看到,在MMRF数据集中，MCL1-M-High和MCL1-M-Low两个亚型同样具有显著不同的预后,MCL1-M-High亚型的总体生存率比MCL1-M-Low亚型要低(log-rank检验，p＝0.006663，似然比检验，风险比1.838，p＝0.00706)。
该结果表明，不管基因表达量数据来自于哪个平台，采用贝叶斯分类器利用MCL1基因群97个基因进行分型，可以用来预测待测患者的预后。
三、数据库GSE19784
根据数据库GSE19784中验证集304例多发性骨髓瘤患者样本(治疗前检测)的MCL1基因群97个分类基因的表达量数据，分别采用实施例1获得的多发性骨髓瘤贝叶斯分类器将该304例样本分为MCL1-M-High(107例)和MCL1-M-Low两个亚型(196例)。
跟踪随访304例样本患者治疗后96个月，根据随访结果，进行生存分析(K-M分析及cox回归分析)，结果如图6所示(A为总体生存率，B为无进展生存率)，可以看到,在GSE19784数据集中，MCL1-M-High和MCL1-M-Low两个亚型同样具有显著不同的预后,MCL1-M-High亚型的总体生存率比MCL1-M-Low亚型要低(log-rank检验，p<0.0001，似然比检验， 风险比1.91，p＝0.0002)。GSE19784数据集也包括疾病的进展信息，因此也分析了无进展生存率的差别，类似的，MCL1-M-High亚型的无进展生存率也比MCL1-M-Low亚型要低log-rank检验，p＝0.0282，似然比检验，风险比1.36，p＝0.031)该结果再次表明，采用贝叶斯分类器利用MCL1基因群97个基因进行分型，可以用来预测待测患者的预后。
实施例3、多发性骨髓瘤的分子诊断标志物及分型在预测待测患者是否能够用硼替佐米治疗
GSE19784多发性骨髓瘤的表达量数据集来自于一项III期药物的临床试验(HOVON-65/GMMG-HD4)，附有病人的药物治疗方案。该试验把病人随机分到了两组分别接受VAD(155例)和PAD(148例)两种药物组合，两者的差别在于PAD方案多了硼替佐米(商品名：万珂)这种药物。入组病人的基因表达量数据都采集于治疗前。
采用实施例1获得的多发性骨髓瘤贝叶斯分类器将该样本分为MCL1-M-High和MCL1-M-Low两个亚型，然后分别在MCL1-M-High(PAD 51例，VAD 56例)和MCL1-M-Low(PAD 104例，VAD 92例)两个亚型中按药物治疗方案进分组进行了生存分析(K-M分析及cox回归分析)。
结果如图7所示,A为MCL1-M-High组的总体生存率，B为MCL1-M-Low组的总体生存率，C为MCL1-M-High组的无进展生存率，D为MCL1-M-Low组的无进展生存率；可以观察到，使用硼替佐米的PAD药物仅能延长MCL1-M-High组中患者的生存期，尤其是无进展生存期(图7左侧，MCL-M-High组，右侧MCL-M-Low组；上方，总体生存曲线，下方，无进展生存曲线)，这揭示了硼替佐米在临床上能够延缓MCL-M-High组病人的复发恶化，但在MCL-M-Low组病人中却没有任何效果。综上所述，实施本发明的分子分型可以指导临床用药，可以避免在MCL1-M-Low组病人中使用硼替佐米，一方面可以减轻患者的经济负担，一方面也能使患者少承担药物治疗带来的副作用。
实施例4、多发性骨髓瘤的贝叶斯分类器在预测患者风险分层中的应用
新收集了30例新诊断的多发性骨髓瘤患者的骨髓样本。使用连接有CD138抗体的磁珠从骨髓中纯化出CD138阳性多发性骨髓瘤细胞，并抽提 多发性骨髓瘤细胞的总RNA。使用昂非PrimeView芯片与这些RNA样本进行杂交，检测了上述97个多发性骨髓瘤分型基因的表达量。
分别使用上述Consensus Clustering聚类算法建立标准分型(MCL1-M-High亚型和MCL-M-Low亚型)，并使用实施例1的贝叶斯分类器个体化预测每一病人的分子亚型。
结果如表4所示，表明个体化预测的亚型与聚类算法建立的亚型之间一致性很高，仅有1例样本被预测为MCL1-M-High亚型患者被预测为MCL-M-Low亚型。这表明的方法可应用与对单独一个患者进行MC1-M亚型判定。
由于样本较少，且随访期较短，没有进行生存曲线分析。但对传统的风险指标(现有医学认定指标)进行分析发现。19例MCL-M-High患者有14例是高危，而11例MCL-M-Low患者仅有3例为高危。这表明在本实例中，所建立的分型仍然可以预测患者的预后状况。
表4.利用GSE2658数据集建立的分类器在搜集的样本中的准确度
Figure PCTCN2019084241-appb-000009
工业应用
为了克服现有多发性骨髓瘤分型方案不结合细胞学起源、不能预测药物疗效的缺陷，发明人探索了围绕生发中心(GC)浆细胞发育过程中保守的关键信号通路的基因共表达网络是否能够辅助阐明MM发病机制并应用于MM的分子分型。发明人重点寻找了多发性骨髓瘤中控制B细胞向浆细胞分化过程中失调的基因网络，因为它可能在多发性骨髓瘤的形成中起到 关键作用。经过上述分析，鉴定出了一个与MCL1基因共表达的基因模块(简称为MCL1-M)，并应用它将多发性骨髓瘤分成为两个主要亚型，即MCL-M-High亚型和MCL-M-Low亚型。这两个亚型具有显著不同的预后与遗传学特征，更重要的是，该分类系统还能预测病人对硼替佐米的治疗的反应并且与浆细胞的发育阶段相关。这些发现能为今后个体化精准治疗的实施铺平了道路，也能提高对多发性骨髓瘤病因的理解。

Claims (17)

  1. 获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质在制备预测待测多发性骨髓瘤肿瘤患者预后情况的产品中的应用；
    所述97个基因为如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36、ZNF593。
  2. 根据权利要求1所述的应用，其特征在于：
    所述预后生存情况体现在预后生存率大小、生存期长短或存活风险程度。
  3. 获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质在制备具有如下a-c中至少一种功能产品中的应用：
    a、检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
    b、检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物的敏感度；
    c、指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药；
    所述97个基因为如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、 MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36、ZNF593。
  4. 获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备在制备预测待测多发性骨髓瘤肿瘤患者预后情况的产品中的应用；
    所述97个基因为如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36、ZNF593；
    所述多发性骨髓瘤贝叶斯分类器按照包括如下步骤的方法获得：
    1)获得n个多发性骨髓瘤样本的97个基因的表达量数据；
    2)将所述n个多发性骨髓瘤样本的97个基因的表达量数据用Consensus Clustering聚类算法分为MCL1-M-High和MCL1-M-Low两个亚型；
    3)基于步骤2的两个亚型、步骤1)的n个多发性骨髓瘤样本的97个基因的表达量数据、n个多发性骨髓瘤样本的预后生存期数据，用朴素贝叶斯方法构建得到贝叶斯分类器。
  5. 根据权利要求4所述的应用，其特征在于：
    所述预后生存情况体现在预后生存率大小、生存期长短或存活风险程 度。
  6. 获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备在制备具有如下a-c中至少一种功能产品中的应用：
    a、检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
    b、检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物的敏感度；
    c、指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药；
    所述97个基因为如下：ACBD3、ADAR、ADSS、ALDH2、ANP32E、ANXA2、ATF3、ATP8B2、CACYBP、CAPN2、CCND1、CCT3、CDC42SE1、CERS2、CHSY3、CLIC1、CLMN、COPA、CSNK1G3、DAP3、DENND1B、ENSA、EPRS、EPSTI1、EVL、FAM13A、FAM49A、FLAD1、FRZB、GLRX2、HAX1、HDGF、HLA-A、HLA-B、HLA-C、HLA-F、HLA-G、IL6R、ISG20L2、JTB、KLF2、LAMTOR2、LDHA、MCL1、MOXD1、MRPL24、MRPL9、MVP、MYL6、NDUFS2、NOP58、NOTCH2NL、NTAN1、PAK1、PI4KB、PIEZO1、PIK3AP1、PIM2、PIP5K1B、PMVK、POGZ、PPIA、PRCC、PRKCA、PRRC2C、PSMB4、PSMD4、RAB29、RCBTB2、SCAMP3、SCAPER、SDHC、SEL1L3、SELPLG、SHC1、SIDT1、SSR2、STAP1、TAP1、TIMM17A、TLR10、TMCO1、TOR1AIP2、TOR3A、TP53INP1、TPM3、TRANK1、TROVE2、UAP1、UBE2Q1、UBQLN4、UHMK1、VPS45、YY1AP1、ZC3H11A、ZFP36、ZNF593；
    所述多发性骨髓瘤贝叶斯分类器按照包括如下步骤的方法获得：
    1)获得n个多发性骨髓瘤样本的97个基因的表达量数据；
    2)将所述n个多发性骨髓瘤样本的97个基因的表达量数据用Consensus Clustering聚类算法分为MCL1-M-High和MCL1-M-Low两个亚型；
    3)基于步骤2的两个亚型、步骤1)的n个多发性骨髓瘤样本的97个基因的表达量数据、n个多发性骨髓瘤样本的预后生存期数据，用朴素贝叶斯方法构建得到贝叶斯分类器。
  7. 一种产品，包括获得或检测待测多发性骨髓瘤肿瘤患者中97个基 因表达的物质和运行多发性骨髓瘤贝叶斯分类器的设备。
  8. 根据权利要求7所述的产品，其特征在于：
    所述产品具有如下1)-4)中至少一种功能：
    1)预测待测多发性骨髓瘤肿瘤患者预后情况；
    2)检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物敏感情况；
    3)检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
    4)指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药。
  9. 根据权利要求7或8所述的产品，其特征在于：
    所述产品还包括记载检测方法的载体；
    所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后情况显著差于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
    或，所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物治疗效果好于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
    或，所述检测方法包括如下步骤：用所述获得或检测待测多发性骨髓瘤肿瘤患者中97个基因表达的物质得到所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，若待测多发性骨髓瘤肿瘤患者属于MCL1-M-High亚型，则用硼替佐米或含有硼替佐米的药物治疗；若待测多发性骨髓瘤肿瘤患者属于MCL1-M-Low亚型，则不用 硼替佐米或含有硼替佐米的药物治疗。
  10. 根据权利要求7-9中任一所述的产品，其特征在于：所述待测多发性骨髓瘤肿瘤患者为单个患者或多个患者。
  11. 构建对多发性骨髓瘤肿瘤患者进行分型的模型的方法，包括如下步骤：
    1)获得n个多发性骨髓瘤样本的97个基因的表达量数据；
    2)将所述n个多发性骨髓瘤样本的97个基因的表达量数据用Consensus Clustering聚类算法分为MCL1-M-High和MCL1-M-Low两个亚型；
    3)基于步骤2)的两个亚型、步骤1)的n个多发性骨髓瘤样本的97个基因的表达量数据、n个多发性骨髓瘤样本的预后生存期数据，用朴素贝叶斯方法构建得到贝叶斯分类器，即为目的模型。
  12. 由权利要求11所述方法制备的素贝叶斯分类器。
  13. 权利要求12的素贝叶斯分类器在制备如下1)-4)至少一种或制备1)-4)至少一种产品中的应用：
    1)预测待测多发性骨髓瘤肿瘤患者预后情况；
    2)检测待测多发性骨髓瘤肿瘤患者对硼替佐米或含有硼替佐米的药物敏感情况；
    3)检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果；
    4)指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药。
  14. 一种预测待测多发性骨髓瘤肿瘤患者预后情况方法，包括如下步骤：
    获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类；
    属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后情况显著劣于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
  15. 根据权利要求14所述的方法，其特征在于:
    所述预后情况体现在预后生存率大小、生存期长短或存活风险程度；
    所述属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后生存率显著劣于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者体现为如下1)-3)中至少一种：
    1)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预测预后生存率显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
    2)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者预后生存期显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者；
    3)属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者存活风险程度显著低于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
  16. 一种检测待测多发性骨髓瘤肿瘤患者用硼替佐米或含有硼替佐米的药物治疗效果，包括如下步骤：获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物治疗效果好于属于MCL1-M-Low亚型的待测多发性骨髓瘤肿瘤患者。
  17. 一种指导待测多发性骨髓瘤肿瘤患者硼替佐米或含有硼替佐米的药物用药，包括如下步骤：获得所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据；再将所述待测多发性骨髓瘤肿瘤患者97个基因的表达量数据用所述多发性骨髓瘤贝叶斯分类器进行分类，对属于MCL1-M-High亚型的待测多发性骨髓瘤肿瘤患者采用硼替佐米或含有硼替佐米的药物治疗。
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