WO2021227950A1 - Procédé de pronostic du cancer - Google Patents

Procédé de pronostic du cancer Download PDF

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WO2021227950A1
WO2021227950A1 PCT/CN2021/092132 CN2021092132W WO2021227950A1 WO 2021227950 A1 WO2021227950 A1 WO 2021227950A1 CN 2021092132 W CN2021092132 W CN 2021092132W WO 2021227950 A1 WO2021227950 A1 WO 2021227950A1
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cancer
patient
methylation
tissue
tissues
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Chinese (zh)
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王晨阳
林静
李冰思
揣少坤
张之宏
汉雨生
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广州燃石医学检验所有限公司
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • 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
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • the present disclosure generally relates to the field of biological detection and diagnosis. Specifically, the present disclosure relates to a method for prognosing a patient based on the difference in methylation in the genome of the cancer tissue sample and the adjacent tissue sample of the patient. The present disclosure also relates to systems and devices for prognosing cancer patients.
  • cancer is one of the main causes of death and disease burden.
  • Cancer prognosis is the prediction of the possible outcomes of an individual's current medical condition, and it is an important tool for improving patient diagnosis and treatment management.
  • An accurate prognosis is essential for choosing the right cancer treatment and predicting survival rates.
  • the clinical stage and pathological analysis of tumors are mainly used to assist some related molecular characteristics (such as immunohistochemistry, DNA mutations, mRNA or microRNA expression, etc.) to evaluate and predict the prognosis of patients.
  • molecular assessment methods still have great limitations.
  • molecular markers of 21 genes in breast cancer can accurately predict the risk of postoperative recurrence of patients and bring significant clinical benefits to breast cancer patients.
  • DNA methylation mutations are closely related to the occurrence of cancer, and compared with gene mutations, DNA methylation mutations have the characteristics of wider coverage, higher stability, and earlier occurrence. , So it is more suitable for early detection of cancer.
  • methods and strategies for predicting the prognosis of cancer patients using DNA methylation mutations are still very lacking.
  • regional carcinogenesis believes that under the action of a certain mechanism, normal tissues will gradually begin the process of carcinogenesis at the molecular level, and this change will first appear in the DNA. Based on the theory of "regional carcinogenesis", the inventors designed a new system that uses DNA methylation detection technology to predict the prognostic recurrence risk of cancer patients-malignancy density ratio (MD ratio) Evaluation system.
  • MD ratio tumor-malignancy density ratio
  • the MD ratio evaluation system is based on the following theory, that is, normal tissues in patients are in the process of transforming from normal cells to cancer cells. Through the detection of methylation mutations, the process of canceration can be evaluated, so as to predict the risk of tumor recurrence in patients. Compared with traditional testing, the MD ratio evaluation system predicts the patient’s recurrence risk only by testing tissue samples from the patient, which can better manage the patient’s prognosis, avoid frequent follow-up after surgery, and is simple and efficient. , Personalized features, so it has a better application prospect in prognosis management. Moreover, in the analysis of real samples, the system has higher accuracy than mutation detection in predicting recurrence.
  • the present disclosure relates to a method for prognosing cancer patients, the method comprising:
  • the method includes:
  • DMB differential methylation block
  • MB methylated block
  • ⁇ i (T) M i (T) / N i (T)
  • beta] i (A) M i (A) /N i (A)
  • DMB DMB
  • combining adjacent CpG sites in step b2 can be, for example, combining a distance of less than 50 bp, a distance of less than 100 bp, a distance of less than 150 bp, a distance of less than 200 bp, a distance of less than 250 bp, a distance of less than 300 bp, and a distance of less than 350 bp.
  • CpG sites with a distance of less than 50 bp or a distance of less than 100 bp are combined in step b2.
  • determining the MB with a significant difference between ⁇ i (T) and ⁇ i (A) as DMB can be, for example, determining
  • step c1 the patient's alpha value is calculated by the following algorithm:
  • l( ⁇ ) is the log-likelihood function of the observation data
  • p i (0) is the Beta-binomial distribution subject to the baseline methylation level of the i-th MB (p i (0 ) , The shape parameter in q i (0) ).
  • one or more regions of the genome are regions of the genome where there are methylation variants in the population of cancer patients.
  • the region of the genome that is known to have methylation variants in a patient population of a specific cancer type can be obtained through a public database and used in the detection method of the present disclosure.
  • the aforementioned public databases are, for example, the TCGA (The Cancer Genome Atlas) database and the GEO (Gene Expression Omnibus) database.
  • one or more regions of the genome cover a region of at least 0.3M (megabases), such as at least 0.3M, 0.4M, at least 0.5M, at least 0.6M, at least 0.7M, at least 0.8M , At least 0.9M or at least 1.0M area.
  • one or more regions of the genome cover about 0.3M-10.0M regions of the genome, such as 0.3M-5.0M, 0.3M-4.0M, 0.3M-3.0M regions, 0.3M-3.0M regions, 2.0M area, 0.3M-1.5M area, 0.3M-1.0M area, 0.4M-5.0M area, 0.4M-4.0M area, 0.4M-3.0M area, 0.4M-2.0M Area, 0.4M-1.5M area, 0.4M-1.0M area, 0.5M-5.0M area, 0.5M-4.0M area, 0.5M-3.0M area, 0.5M-2.0M area , 0.5M-1.5M area, 0.5-1.0M area, or 1.0M-5.0M area, 1.0M-4.0M area, 1.0M-3.0M area, 1.0M-2.0M area or 1.0 M-1.5M area.
  • the above range also includes endpoint values and any subset ranges in between.
  • the cancer is a solid tumor.
  • solid tumors include, but are not limited to, lung cancer (including small cell lung cancer, non-small cell lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma), colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, gallbladder cancer, gastric cancer, esophageal cancer , Kidney cancer, melanoma, breast cancer, cervical cancer, endometrial cancer, prostate cancer, bladder cancer, testicular cancer, thyroid cancer, salivary gland cancer, skin cancer, squamous cell carcinoma, neuroblastoma, glioblastoma Tumor, retinoblastoma, lymphoma (including Hodgkin’s lymphoma and non-Hodgkin’s lymphoma), bone cancer, myeloma, basal cell carcinoma, peritoneal cancer, choriocarcinoma, eye cancer, head and neck cancer, laryngeal cancer , Oral
  • the cancer may be selected from lung cancer, colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, gallbladder cancer, gastric cancer, and esophageal cancer.
  • the cancer is a primary cancer. In other embodiments, the cancer is a secondary or metastatic cancer.
  • the cancer may be in any stage of cancer development, such as early, middle or late stages of cancer development, or the cancer may be in clinical stages I, II, III, or IV.
  • the cancer is lung cancer, such as non-small cell lung cancer (NSCLC).
  • NSCLC non-small cell lung cancer
  • one or more regions of the detected genome may include one or more regions selected from those listed in Table 1.
  • the cancer is lung cancer
  • one or more regions of the detected genome include at least 100 regions, at least 200 regions, at least 300 regions, and at least 400 regions selected from those listed in Table 1. , At least 500 areas, at least 600 areas, at least 700 areas, at least 800 areas, at least 900 areas, or at least 1000 areas.
  • the one or more regions of the detected genome include all regions selected from the list in Table 1.
  • the cancer patient has undergone previous cancer treatment methods, such as surgical treatment, radiation therapy, chemotherapy, targeted drug therapy, immunotherapy, or a combination thereof.
  • the cancer tissue and para-cancerous tissue used may be tissues surgically removed from the patient.
  • using methylation data from normal tissues includes:
  • methylated normal tissue data referred to as M i (0) and N i (0), that at a given N i (0) of M i (0) is subject to a shape parameter ( Beta-binomial distribution of p i (0) , q i (0) ):
  • the p-value is calculated by Wald test in step c3. In other embodiments, the p-value is calculated by, for example, a likelihood ratio test.
  • the method is used to predict the postoperative recurrence risk and/or survival of the cancer patient.
  • patients with p ⁇ 0.05 are identified as having a high risk of recurrence and/or low postoperative survival.
  • the present disclosure relates to a system for prognosing cancer patients, the system including:
  • the methylation sequencing module is configured to detect the methylation level in one or more regions of the genome of cancer tissue and paracancerous tissue from the patient through high-throughput sequencing
  • the prognostic analysis module is configured For the prognosis of the patient through the following methods:
  • the prognosis of the patient is performed by mathematical modeling
  • system is configured to implement the prognostic method according to the first aspect of the present disclosure.
  • the prognostic analysis module is configured to prognose the patient through the following methods:
  • DMB differential methylation block
  • ⁇ i (T) M i (T) / N i (T)
  • beta] i (A) M i (A) /N i (A)
  • DMB DMB
  • the prognostic analysis module is configured to set a distance of less than 50 bp, a distance of less than 100 bp, a distance of less than 150 bp, a distance of less than 200 bp, a distance of less than 250 bp, a distance of less than 300 bp, a distance of less than 350 bp, and a distance of less than 400 bp in step a2.
  • the prognostic analysis module is configured to combine CpG sites with a distance of less than 50 bp or a distance of less than 100 bp in step b2.
  • the prognostic analysis module is configured to determine
  • the prognostic analysis module is configured to calculate the alpha value of the patient through the following algorithm in step b1:
  • l( ⁇ ) is the log-likelihood function of the observation data
  • p i (0) is the Beta-binomial distribution subject to the baseline methylation level of the i-th MB (p i (0 ) , The shape parameter in q i (0) ).
  • the prognostic analysis module is further configured to use methylation data from normal tissues to establish the baseline methylation level, including:
  • methylated normal tissue data referred to as M i (0) and N i (0), that at a given N i (0) of M i (0) is subject to a shape parameter ( Beta-binomial distribution of p i (0) , q i (0) ):
  • system is further configured to calculate the p-value by Wald test in step b3.
  • the p-value is calculated by, for example, a likelihood ratio test.
  • the system is further configured to predict the postoperative recurrence risk and/or survival of the cancer patient.
  • patients with p ⁇ 0.05 are identified as having a high risk of recurrence and/or low postoperative survival.
  • the present disclosure relates to a device for prognosing cancer patients, which includes:
  • Memory for storing computer program instructions
  • a processor for executing computer program instructions
  • the device executes the method according to the first aspect of the present disclosure.
  • the present disclosure relates to a computer-readable medium storing computer program instructions, wherein when the computer program instructions are executed by a processor, the computer program instructions according to the first aspect of the present disclosure are implemented. The method described.
  • Figure 1 shows the line chart of the disease-free survival (DFS) results of patients with high risk of recurrence and low risk of recurrence predicted by detecting the cancer tissue and the genomic regions listed in Table 1 in the patient's cancer tissue and paracancerous group using the MD ratio method .
  • DFS disease-free survival
  • Figure 2 shows the line graph of the disease-free survival (DFS) results of patients with high risk of recurrence and low risk of recurrence predicted by detecting the cancer tissue and the genomic regions listed in Table 3 in the patient's cancer tissue and paracancerous group using the MD ratio method .
  • DFS disease-free survival
  • Figure 3 shows a line chart of the disease-free survival (DFS) results of patients with high risk of recurrence and low risk of recurrence predicted by detecting the cancer tissue and the genomic regions listed in Table 4 in the patient's cancer tissue and paracancerous group using the MD ratio method .
  • DFS disease-free survival
  • the main strategy of the MD ratio assessment system is to sequence the methylation levels of tissue samples to find areas with differences in methylation status in the genome of cancer tissues and normal tissues (such as paracancerous tissues) in patients. Based on the paracancerous tissues in the process of transforming from normal cells to cancer cells, calculate the degree of similarity between the methylation level of the paracancerous tissues and the methylation level of cancer tissues in the different regions, and then infer the cancerous transformation of normal cells in the patient Risk level.
  • the MD ratio assessment system for cancer recurrence risk detects the patient’s cancer tissue and para-cancerous tissue samples. Take lung cancer as an example.
  • the adjacent tissue is defined as the tissue outside the resection margin 5cm; in the wedge resection, the adjacent tissue is defined as the tissue outside the resection margin 3cm, and pass
  • the pathological evaluation of the tissue cells verifies that they do not contain tumor cells; at the same time, the cell types of the cancer tissue and the adjacent tissues are the same.
  • the sample library is prepared using the brELSATM method (Burning Rock Biotech, Guangzhou, China), which includes the following steps: 1) DNA extraction and purification; 2) sodium bisulfite treatment; 3) single-stranded DNA amplification by DNA polymerase; 4) Use a customized cancer methylation profile RNA bait to enrich the target region as shown in Table 1 (covering a region of about 0.9M of the human genome); 5) Quantify the target library by real-time PCR. Finally, the sequencer NovaSeq 6000 released by Illumina was used for sequencing, and the average sequencing depth was 1,000 layers.
  • the original output files of sequencing were analyzed using sequence comparison software BWA-meth and methylation data statistics software MethylDackel to obtain the methylation detection output files of each sample. It contains the location information of each CpG site in the specific capture area, and the methylation information in the reads covering this site. The number of methylated reads covering this site is recorded as M, and the number of unmethylated reads is recorded as U. Combining adjacent CpG sites (with a distance of less than 50 bp), this set of multiple CpG sites is called a methylation block (MB).
  • MB methylation block
  • M i M i + U i
  • N i M i + U i
  • the research target is targeted at the regions of the cancer and para-cancerous tissues where there is a difference in methylation in the genome, which is defined as a differentially methylated block ( differential methylated block, referred to as DMB).
  • DMB differential methylated block
  • the methylation level ⁇ (T) of the cancer tissue sample of the patient and the methylation level ⁇ (A) of the adjacent tissue sample are used for testing.
  • ⁇ i (T) M i (T) / N i (T)
  • ⁇ i (A ) M i (A) /N i (A) , which will conform to
  • the MB is determined to be the patient’s personalized DMB.
  • methylated normal tissue data referred to as M i (0) and N i (0), that at a given N i (0) of M i (0) is subject to a shape parameter ( Beta-binomial distribution of p i (0) , q i (0) ):
  • ⁇ i (0) represents the baseline of the actual degree of methylation using the maximum likelihood algorithm (maximum likelihood estimation, referred MLE) solving the shape parameter ⁇ p i, q i ⁇ , the maximum likelihood estimates of the parameters in mind It is ⁇ p i (0) , q i (0) ⁇ .
  • methylation sequencing data (M i (A), N i (A), ⁇ i (A)) which is adjacent to obey a cancerous tissue sample hybrid Beta-Binomial distribution
  • M i (A) N i (A), ⁇ i (A)
  • Beta-Binomial distribution may be specifically expressed as:
  • ⁇ i (T) represents the methylation level of the cancer tissue of the patient, which can be estimated by the moment of the sequencing data of the cancer tissue sample Place;
  • ⁇ i (0) represents the baseline level of methylation, as described above, which is subject to the shape parameter (p i (0), q i (0)) of the Beta distribution.
  • ⁇ is a ratio parameter on [0,1], which indicates the similarity between adjacent tissues and cancerous tissues. The closer ⁇ is to 1, the closer the degree of methylation of the adjacent tissues is to the cancer tissue, and it can be inferred that the patient's risk of recurrence is higher.
  • the log likelihood function of the observation data can be written:
  • the values of ⁇ are 0, 0.001, 0.003, 0.01, 0.003, 0.1, and each group of parameters is repeated 50 times.
  • the numerical simulation results are shown in Table 2 below.
  • the genomic regions listed in Table 4 and Table 5 in cancer tissues and adjacent tissues (which include 522 and 532 randomly selected from Table 1 respectively) These regions cover the methylation levels of 0.47M and 0.48M in the genome, respectively.
  • the patient's p-value is calculated by the same algorithm, and the test results are divided into high-risk recurrence and low-risk recurrence according to the test p-value ⁇ 0.05.
  • the disease-free survival (DFS) results are shown in Figure 2 and Figure 3, respectively.
  • MD ratio-1 represents the result obtained by detecting the area shown in Table 1 (corresponding to Figure 1); MD ratio-2 and MD-ratio-3 represent the result obtained by detecting the area shown in Table 3 and Table 4, respectively (Corresponding to Figure 2 and Figure 3 respectively).
  • the above results indicate that the MD ratio assessment system can more effectively assess the patient's cancer recurrence risk and subsequent survival than the somatic test, and play a better role in the patient's prognosis management and clinical treatment.

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Abstract

L'invention concerne un procédé de pronostic pour un patient atteint d'un cancer. Le risque de rechute et/ou la survie d'un patient sont prédits sur la base d'une différence de méthylation dans les génomes d'un échantillon de tissu cancéreux et d'un échantillon de tissu para-cancéreux du patient. L'invention concerne en outre un système et un dispositif de pronostic pour un patient atteint d'un cancer.
PCT/CN2021/092132 2020-05-09 2021-05-07 Procédé de pronostic du cancer WO2021227950A1 (fr)

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