WO2020168541A1 - 肠道宏基因组在筛选pd-1抗体阻断剂疗效方面的用途 - Google Patents

肠道宏基因组在筛选pd-1抗体阻断剂疗效方面的用途 Download PDF

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WO2020168541A1
WO2020168541A1 PCT/CN2019/075825 CN2019075825W WO2020168541A1 WO 2020168541 A1 WO2020168541 A1 WO 2020168541A1 CN 2019075825 W CN2019075825 W CN 2019075825W WO 2020168541 A1 WO2020168541 A1 WO 2020168541A1
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lung cancer
marker
relative abundance
sequencing
small cell
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French (fr)
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方超
钟焕姿
侯勇
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BGI Shenzhen Co Ltd
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BGI Shenzhen Co Ltd
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Priority to CN201980076831.8A priority patent/CN113166815B/zh
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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
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • 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
    • 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
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • 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
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • 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
    • G01N33/575—Immunoassay; Biospecific binding assay; Materials therefor for cancer

Definitions

  • the invention relates to the field of biological detection, in particular to the use of an intestinal metagenomics in screening the curative effect of PD-1 antibody blockers.
  • PD-1 Programmed apoptosis protein 1 receptor
  • T cells T cells, B cells, macrophages, regulatory T cells, and natural killer cells.
  • PD-1 can be bound by a variety of ligands, the main two types are called PD-1 ligand 1 and PD-1 ligand 2.
  • the PD-1 signaling pathway activated by ligand binding can inhibit T cell proliferation and immune response activities. Therefore, activating the PD-1 signaling pathway has become the main means for tumor cells to escape the antigen-specific T cell immune response.
  • the PD-1 antibody blocker developed for this phenomenon can block this process, thereby enhancing the body's own anti-tumor immunity activity.
  • the present invention aims to solve one of the technical problems in related technologies at least to a certain extent, and provides a method for screening the curative effect of PD-1 antibody blockers using the intestinal metagenomics.
  • the application of the intestinal metagenomics to predict the therapeutic effect of PD-1 antibody blockers on cancer can guide clinical medication, so as to achieve precise treatment of individuals.
  • the present invention screens to determine the flora markers that can effectively assist the therapeutic effect of PD-1 antibody blockers on specific tumor patients; at the same time, the relative content of intestinal flora markers is used to construct a model to effectively predict the patient’s acceptance of the species. The expected response level of the therapy.
  • the present invention provides the use of the intestinal metagenomics in predicting the therapeutic effect of PD-1 antibody blockers on cancer.
  • the metagenome also called the environmental microbial genome or metagenome, is the sum of all genetic genes in a specific environment.
  • the gut metagenome refers to the sum of all genetic genes in the gut environment.
  • the use of the aforementioned intestinal metagenomics in predicting the therapeutic effect of PD-1 antibody blockers on cancer may further include the following technical features:
  • metagenomics is used to analyze the components or functional changes of the intestinal metagenomics to predict the therapeutic effect of PD-1 antibody blockers on cancer.
  • metagenomics refers to the microbial population genome in environmental samples as the research object. Through modern genomics technology, including the screening and sequencing analysis of functional genes, it can analyze the microbial diversity, population structure, evolutionary relationship and function of the environment. A new microbial research method for studying the relationship between activity, mutual cooperation and the environment.
  • the metagenomics is an omics study based on data by identifying microorganisms in environmental samples and quantifying their richness through DNA sequencing.
  • the metagenomics is whole-gene sequencing metagenomics.
  • Whole gene sequencing Metagenomics (whole genome metagenomics) is to sequence all microorganisms in a specific environment as a whole. Using whole-gene sequencing metagenomics can study the functional genes or functional microorganisms in samples and the role of functional genes or functional microorganisms in the environment.
  • the metagenomics is 16S/18S rDNA sequencing, or probe sequencing.
  • 16S rDNA is a gene encoding the small subunit 16S rRNA of prokaryotic ribosomes.
  • the degree of variation in the hypervariable region is closely related to the phylogeny of prokaryotic microorganisms.
  • Using high-throughput sequencing technology to sequence the 16S rDNA hypervariable region can analyze the diversity of the community structure of prokaryotic microorganisms in the environment.
  • 18S rDNA is a gene encoding the small eukaryotic ribosomal subunit 18S rRNA, which also has conserved and hypervariable regions.
  • the 18S rDNA hypervariable region was sequenced through high-throughput sequencing technology to analyze the colony structure diversity of eukaryotic microorganisms in the environment.
  • Probe sequencing is to capture target regions by designing probes, such as capturing all exon regions of microorganisms in the intestinal metagenomics, to analyze and study the relationship between these regions and diseases.
  • the intestinal metagenomic group includes intestinal microorganisms.
  • the intestinal metagenomics includes all the genetic material in the intestine. A large part of these genetic material comes from gut microbes. Through the analysis of intestinal microbes, the changes in the type or content of intestinal microbes can be used to predict the therapeutic effect of PD-1 antibody blockers on cancer and guide clinical medication.
  • the changes in the composition of the intestinal metagenomics include changes in the type or content of intestinal microorganisms.
  • the functional changes of the intestinal metagenomics include changes in metabolic function genes.
  • Metabolic genes refer to genes related to the metabolic function of the intestine. These genes can be expressed as enhancing the metabolic function of the intestine or reducing the metabolic function of the intestine. For example, changes in intestinal metabolic function can be manifested as changes in the microbial environment in the intestines, thereby showing differences in response efficiency to PD-1 antibody blockers.
  • the cancer includes at least one selected from the group consisting of non-small cell lung cancer, colorectal cancer, liver cancer, esophageal cancer, lung cancer, breast cancer, and gastric cancer.
  • these cancers usually appear as malignant tumors, and during treatment with PD-1 antibody blockers, different patients usually show different response rates.
  • a review published in 2016 Zou, W, et al, 2016
  • 22 projects involving kidney cancer, non-small cell lung cancer, and colorectal cancer were treated with PD-1 blockers before 2015.
  • the response rate fluctuated from 12.8% to 43.7%.
  • the PD-1 antibody blocking agent includes at least one selected from the group consisting of pembrolizumab, nivolumab, and Camrelizumab.
  • the present invention provides a marker comprising at least one selected from the group consisting of Streptococcus sanguinis (Streptococcus sanguinis) and/or its analogues, Escherichia coli (E.
  • Clostridium sp.L2-50 Clostridium sp.L2-50 and/or its analogues
  • Citrobacter freundii (Citrobacter freundii) and/or its analogues
  • Pyramidobacter piscolens conetobacter sp.L2-50) piscolens bacteria
  • Streptococcus sanguinis analogue and the Streptococcus sanguinis genome sequence are more than 85% similar
  • the Escherichia coli analogue is compared with the Escherichia coli genome sequence.
  • the Clostridium sp.L2-50 analogue is more than 85% similar to the Clostridium sp.L2-50 genome sequence.
  • the Citrobacter freundii analogue is similar to the Citrobacter freundii Compared with the genome sequence, the alignment similarity is more than 85%, and the Pyramidobacter piscolens analog is more than 85% compared with the Pyramidobacter piscolens genome sequence.
  • each bacterial analogue when the comparison coverage of each bacterial analogue is more than 80% compared with the corresponding bacterial genome sequence, and the comparison similarity is more than 85%, these analogues can be considered It belongs to the same genus as the corresponding bacteria and can be used as a marker.
  • the comparison coverage of these analogs and the corresponding bacteria is more than 80%, and the comparison similarity is more than 95%, it can be considered that these analogs are the same species as the corresponding bacteria. landmark.
  • the comparison coverage in the present invention refers to the ratio of the length of the sequence compared with the reference sequence in the target sequence to the total length of the detection sequence in the process of comparing the target sequence with the reference sequence.
  • the present invention provides a preparation comprising Escherichia coli (E. coli) and/or its analogues, Citrobacter freundii (Citrobacter freundii) and/or At least one of the analogs is prepared.
  • Escherichia coli E. coli
  • Citrobacter freundii Citrobacter freundii
  • At least one of the analogs is prepared.
  • Each marker strain can be separated and cultured, combined with auxiliary materials, or directly lyophilized to form a freeze-dried powder, to obtain a preparation, which can intervene and regulate the intestinal tract of patients with non-small cell lung cancer to improve PD -1 The effect of immunotherapy on patients.
  • the formulation includes at least one selected from the group consisting of tablets, pills, powders, injections, solutions, suspensions, capsules, and granules.
  • the present invention provides a method for predicting the therapeutic effect of a PD-1 antibody blocker on patients with non-small cell lung cancer, including: (1) collecting samples from the patients; (2) The sample is tested to determine the relative abundance information of the marker in the sample, the marker is the marker according to the second aspect of the present invention; (3) the relative abundance of the marker in the sample The abundance information is compared with a reference data set or reference value to predict the therapeutic effect of the PD-1 antibody blocker on the patient.
  • the reference data set includes the relative abundance information of the markers in a plurality of samples of non-small cell lung cancer patients that show an effective response to the PD-1 antibody blocker and a plurality of information on the relative abundance of the markers in the PD-1 antibody blocker.
  • the reference data set in the present invention refers to patients with non-small cell lung cancer who have shown an effective response to PD-1 antibody blockers and non-small cell lung cancer patients who have shown an ineffective response to PD-1 antibody blockers
  • the relative abundance information of each marker obtained by the operation of the patient sample is used as a reference for the relative abundance of each marker.
  • the reference data set refers to the training data set.
  • the training set refers to and the verification set has a well-known meaning in the art.
  • the training set refers to subjects with non-small cell lung cancer who show an effective response to PD-1 antibody blockers and a certain number of samples that show blockade of PD-1 antibodies A data collection of the content of each marker in the test sample of non-small cell lung cancer subjects who did not respond to the drug.
  • the verification set is an independent data set used to test the performance of the training set.
  • the reference value in the present invention refers to the reference value or the normal value of the content of each marker in patients with non-small cell lung cancer that effectively responds to the PD-1 antibody blocker.
  • Those skilled in the art know that when the sample volume of patients with non-small cell lung cancer that responds effectively to PD-1 antibody blockers is large enough, the absolute value of each marker in the sample can be obtained using detection and calculation methods known in the art Range.
  • the detection method is used to detect the level of the marker
  • the measured value of the marker level in the sample can be directly compared with the reference value to evaluate the response effect of the non-small cell lung cancer patient to the PD-1 antibody blocker. For example, statistical methods can be used for analysis and comparison.
  • the above-mentioned method for predicting the therapeutic effect of a PD-1 antibody blocker on patients with non-small cell lung cancer may further include the following technical features:
  • the relative abundance information of the markers in the sample with the reference data set when comparing the relative abundance information of the markers in the sample with the reference data set, it further includes executing a multivariate statistical model to obtain the response probability.
  • the multivariate statistical model is a random forest model.
  • the response probability greater than a predetermined threshold indicates that the PD-1 antibody blocker can effectively treat the patient's non-small cell lung cancer.
  • the predetermined threshold is 0.5.
  • the relative abundance information of the marker in step (2) is obtained by a sequencing method, further comprising: isolating a nucleic acid sample from the sample of the patient; based on the nucleic acid sample, A sequencing library is constructed, and sequencing results are obtained by sequencing; the sequencing results are analyzed to determine the relative abundance information of the markers in the sample.
  • the sequencing results can be compared and analyzed using SOAP2 or MAQ, etc., which can improve the efficiency of comparison, and thus the efficiency of PD-1 antibody blocker response detection.
  • the sample is a stool sample.
  • the sequencing is performed by a second-generation sequencing method or a third-generation sequencing method.
  • the sequencing is performed by at least one selected from the group consisting of Hiseq2000, SOLiD, 454, and single-molecule sequencing devices. of. Utilizing the high-throughput and deep sequencing characteristics of these sequencing devices is beneficial to the analysis of subsequent sequencing data, especially the accuracy and accuracy of statistical tests.
  • the PD-1 antibody blocking agent includes at least one selected from the group consisting of pembrolizumab, nivolumab, and Camrelizumab.
  • the present invention provides a device for predicting the therapeutic effect of a PD-1 antibody blocker on patients with non-small cell lung cancer, including: a sample collection device, the sample collection device being adapted to A sample is collected from a patient; a marker relative abundance determination device, the marker relative abundance determination device is connected to the sample collection device, and the marker relative abundance determination device is adapted to detect the sample to determine Relative abundance information of the marker in the sample, the marker includes the marker according to the second aspect of the present invention; a result determination device, the result determination device is connected to the marker relative abundance determination device, The result determination device is adapted to compare the relative abundance information of the markers in the sample with a reference data set or reference value to predict the therapeutic effect of the PD-1 antibody blocker on the patient; wherein the reference The data set includes information on the relative abundance of the markers in a number of samples from patients with non-small cell lung cancer that showed an effective response to the PD-1 antibody blocker and multiple expressions of the PD-1 antibody blocker Information on
  • the above-mentioned device for predicting the therapeutic effect of PD-1 antibody blocker on patients with non-small cell lung cancer may further include the following technical features:
  • the device when comparing the relative abundance information of the markers in the sample with a reference data set, it further includes executing a multivariate statistical model to obtain the response probability.
  • the multivariate statistical model is a random forest model.
  • the response probability greater than a predetermined threshold indicates that the PD-1 antibody blocker can effectively treat the patient's non-small cell lung cancer; preferably, the predetermined threshold is 0.5.
  • the device for determining relative abundance of markers further includes: a nucleic acid sample separation unit, the nucleic acid sample separation unit is adapted to separate a nucleic acid sample from the sample; a sequencing unit, the sequencing unit and the The nucleic acid sample separation unit is connected, and the sequencing unit constructs a sequencing library based on the nucleic acid sample, and performs sequencing to obtain a sequencing result; a comparison unit, the comparison unit is adapted to analyze the sequencing result to determine the sample The relative abundance information of the markers in.
  • the sample is a stool sample.
  • the sequencing is performed by a second-generation sequencing method or a third-generation sequencing method; preferably, the sequencing is performed by at least one selected from the group consisting of Hiseq2000, SOLiD, 454, and single-molecule sequencing devices .
  • the present invention provides a kit, including reagents for detecting the marker according to the second aspect of the present invention.
  • the kit described above may further include the following technical features:
  • the kit includes at least one set of reference data sets or reference values, which are used as a reference for the relative abundance of each marker.
  • the reference data set or reference value can be attached to some physical carrier such as an optical disk or CD-ROM.
  • the kit further includes a first computer program product, and the first computer program product is used to execute to obtain the reference data set or reference value. That is, the first computer program product is used to execute and obtain the therapeutic effect of the PD-1 antibody blocker on the diagnostic subject.
  • the kit further includes a second computer program product, the second computer program product is used to execute the predicted PD-1 antibody blocker to treat the disease according to any one of the embodiments of the fourth aspect of the present invention Methods of treatment effect for patients with small cell lung cancer.
  • the present invention provides a method for screening PD-1 antibody blockers for non-small cell lung cancer, including: administering candidate PD-1 to subjects suffering from non-small cell lung cancer. 1 After treatment with the antibody blocker, the relative abundance information of the marker in the sample from the subject is determined, and the marker is the marker according to the second aspect of the present invention; The relative abundance information of the marker is compared with the relative abundance information of the marker before the candidate PD-1 antibody blocker is administered to the subject. By administering candidate PD-1 antibody blocker treatment to multiple subjects with non-small cell lung cancer, by detecting changes in the relative abundance information of each marker in the subject, it can be used to indicate candidate PD-1 The effect of antibody blockers on non-small cell lung cancer.
  • the increase in the relative abundance of at least one marker of Escherichia coli and/or its analogs, Citrobacter freundii and/or its analogs indicates that the candidate PD-1 antibody blocker is used Drugs for treating non-small cell lung cancer in the subject, or the method is effective for non-small cell lung cancer in the subject.
  • the relative abundance of at least one marker of Streptococcus sanguinis and/or its analogs, Clostridium sp.L2-50 and/or its analogs, Pyramidobacter piscolens and/or its analogs is reduced.
  • the candidate PD-1 antibody blocker is a drug for treating non-small cell lung cancer in the subject, or the method is effective for non-small cell lung cancer in the subject.
  • the subject is an animal or a human.
  • the present invention provides the use of a marker in the preparation of a kit for predicting the response of patients with non-small cell lung cancer to PD-1 antibody blockers or for screening
  • the PD-1 antibody blocker for non-small cell lung cancer the marker is the marker according to the second aspect of the present invention.
  • the present invention provides a method for establishing a predictive model for predicting the response effect of a PD-1 antibody blocker in patients with non-small cell lung cancer, the method comprising identifying patients with non-small cell lung cancer The step of differentially expressed substances between samples that show an effective response to the PD-1 antibody blocker and those that show an ineffective response to the PD-1 antibody blocker, wherein the differentially expressed substance includes the second selected from the present invention One or more of the markers in the aspect.
  • the beneficial effects achieved by the present invention are: the first successful use of intestinal microbes to screen the efficacy of PD-1 immune blockers in the treatment of non-small cell lung cancer, the intestinal bacteria provided by the present invention or any combination thereof can be more ideally predicted The response rate of patients receiving specific PD-1 blocker immunotherapy.
  • the therapeutic effect of PD-1 antibody blockers on cancer can be predicted, and precise medical treatment of different individuals can be realized.
  • Fig. 1 is a schematic diagram of a device for predicting the therapeutic effect of a PD-1 antibody blocker on patients with non-small cell lung cancer according to an embodiment of the present invention.
  • Fig. 2 is a schematic diagram of a device for determining relative abundance of markers according to an embodiment of the present invention.
  • Fig. 3 is a schematic diagram of a random forest screening, model construction and verification process according to an embodiment of the present invention.
  • a is the error coefficient of variation obtained by random forest screening with different numbers of markers
  • Figure 3 b is the training set, the probability that the combination of 5 markers is effective in the invalid group and the effective group is predicted
  • Figure 3 c is the ROC curve obtained by using the decision model of 5 kinds of marker combinations and random forest
  • Fig. 3 d is the verification set, the probability of the 5 kinds of marker combinations being effective in the invalid group and the effective group is predicted
  • Fig. 3 e In order to use the decision model of 5 kinds of markers and random forest to verify in the verification set, the obtained ROC curve graph.
  • Non-small cell lung cancer is a kind of malignant tumor that is highly malignant and easy to relapse and metastasize, including squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.
  • Lung cancer is the malignant tumor with the highest incidence and mortality in China, and 85% of its patients are non-small cell lung cancer.
  • the application of antibody drugs targeting PD-1/PD-L1 in various solid tumors such as melanoma and non-small cell lung cancer (NSCLC) has achieved remarkable broad-spectrum anti-cancer effects.
  • PD-1 antibody blocker is also called “PD-1 blocker” or "PD-1 blocking inhibitor” refers to a substance that can block the PD-1 signal pathway. These substances are prepared into drugs that can block the PD-1 signaling pathway, thereby preventing tumor cells from escaping the antigen-specific T cell immune response.
  • PD-1 antibody blockers that have been discovered include, but are not limited to: pembrolizumab, nivolumab, and Camrelizumab (which have been launched in China this year, and the last one is a domestically developed blocker).
  • the present invention provides a marker that includes at least one selected from the following Species: Streptococcus sanguinis and/or its analogues, Escherichia coli and/or its analogues, Clostridium sp.L2-50 and/or its analogues, Citrobacter freundii and/or its analogues, Pyramidobacter piscolens and/or its analogues .
  • Streptococcus sanguinis is Streptococcus sanguinis, a gram-positive facultative anaerobe bacterium, commonly found in human oral environment.
  • Escherichia coli is Escherichia coli, commonly found in the intestines of animals and humans, and is a gram-negative facultative anaerobe.
  • Clostridium sp.L2-50 is an unnamed bacterium belonging to the genus Clostridium, which is widely distributed in nature.
  • Citrobacter freundii is Citrobacter freundii, a facultative anaerobic gram-negative bacterium, which is a relatively common bacterium in the intestines of healthy people.
  • Pyramidobacter piscolens is a newly discovered bacteria in human oral cavity in recent years. Most of the bacteria in this genus are anaerobic, non-motile bacilli, and their final secondary metabolites are mainly hydrogen sulfide.
  • biomarker also called “marker”, can also be called “biological marker” or “biological marker”, refers to a measurable indicator of the biological state of an individual.
  • biomarkers can be any substance in the individual, as long as they are related to the specific biological state (e.g., disease) of the subject, for example, nucleic acid markers (also called genetic markers, such as DNA), Protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers), etc.
  • nucleic acid markers is not limited to existing genes that can be expressed as biologically active proteins, but also includes any nucleic acid fragments, which can be DNA, RNA, modified DNA or RNA, or It is unmodified DNA or RNA, and a collection of them. In this context, nucleic acid markers can sometimes be referred to as characteristic fragments. In the present invention, markers can also be replaced by "intestinal markers", because several biomarkers that are closely related to the response of PD-1 antibody blockers discovered in the present invention are all present in the subject's In the intestines. Biomarkers are measured and evaluated, often used to check normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions, and are useful in many scientific fields.
  • the present invention provides a method for predicting the therapeutic effect of a PD-1 antibody blocker on patients with non-small cell lung cancer, including: (1) collecting a sample from the patient; (2) evaluating the The sample is tested to determine the relative abundance information of the markers in the sample; (3) The relative abundance information of the markers in the sample is compared with a reference data set or reference value to predict PD-1 antibody blocking The therapeutic effect of the drug on the patient; wherein the reference data set includes information on the relative abundance of the markers in a plurality of samples from patients with non-small cell lung cancer that show an effective response to the PD-1 antibody blocker And the relative abundance information of the markers in a plurality of samples from patients with non-small cell lung cancer that showed an ineffective response to the PD-1 antibody blocker.
  • high-throughput sequencing can be used to batch analyze stool samples of patients with non-small cell lung cancer that show effective responses to PD-1 antibody blockers and show ineffective responses to PD-1 antibody blockers.
  • Stool samples from patients with non-small cell lung cancer Stool samples from patients with non-small cell lung cancer.
  • samples from patients with non-small cell lung cancer that showed an effective response to PD-1 antibody blockers and samples from patients with non-small cell lung cancer that showed an ineffective response to PD-1 antibody blockers The comparison is performed to determine the specific nucleic acid sequence closely related to the response effect of the PD-1 antibody blocker.
  • the steps are as follows:
  • Sample collection and processing collection of stool samples from non-small cell lung cancer patients who showed an effective response to PD-1 antibody blockers and stool samples from non-small cell lung cancer patients who showed ineffective responses to PD-1 antibody blockers , Use the kit for DNA extraction to obtain nucleic acid samples;
  • DNA library construction and sequencing are performed by high-throughput sequencing to obtain the nucleic acid sequence of gut microbes contained in stool samples;
  • a reference gene set also called a reference gene set, which can be a newly constructed gene set or any database of known sequences, for example, using known non-redundant genes of the human gut microbial community Set
  • stool samples from patients with non-small cell lung cancer who showed an effective response to PD-1 antibody blockers and non-small cell lung cancer patients who showed an ineffective response to PD-1 antibody blockers were determined respectively The relative abundance of each gene in the nucleic acid sample.
  • the corresponding relationship between the sequencing sequence and the genes in the reference gene set can be established, so that for a specific gene in a nucleic acid sample, the number of corresponding sequencing sequences can effectively reflect the gene Relative abundance. Therefore, the relative abundance of genes in the nucleic acid sample can be determined through the comparison results and conventional statistical analysis. Finally, after determining the relative abundance of each gene in the nucleic acid sample, the response to non-small cell lung cancer patients who showed an effective response to PD-1 antibody blockers and non-small cell lung cancer patients who showed an ineffective response to PD-1 antibody blockers was determined. The relative abundance of each gene in the nucleic acid samples of patients with small cell lung cancer is statistically tested.
  • the method for determining species markers and functional markers further includes: comparing patients with non-small cell lung cancer who showed an effective response to PD-1 antibody blockers with those who showed an ineffective response to PD-1 antibody blockers.
  • the sequencing sequence of patients with non-small cell lung cancer was compared with the reference gene set; based on the comparison results, the patients with non-small cell lung cancer that showed an effective response to the PD-1 antibody blocker and the PD-1 antibody blocker were determined respectively.
  • the relative abundance of species and relative abundance of each gene in the nucleic acid samples of patients with non-small cell lung cancer who showed an ineffective response; for patients with non-small cell lung cancer who showed an effective response to PD-1 antibody blockers and for PD The relative abundance of each gene in the nucleic acid samples of non-small cell lung cancer patients who showed an ineffective response to the -1 antibody blocker was tested statistically; and the performance of the PD-1 antibody blocker was determined separately
  • the relative abundance of nucleic acid samples from non-small cell lung cancer patients who responded effectively and non-small cell lung cancer patients who showed an ineffective response to PD-1 antibody blockers has significant differences in species markers and functional markers.
  • the relative abundance of genes from the same species and the relative abundance of genes with the same functional annotation can be used to perform statistical tests, such as summation, average value, median value, etc., to determine the function Relative abundance and relative abundance of species.
  • the term "existence" used in this article should be understood in a broad sense. It can refer to qualitative analysis of whether the sample contains the corresponding target, or it can refer to the quantitative analysis of the target in the sample, and it can further
  • the obtained quantitative analysis result is the result of statistical analysis or any known mathematical operation with the reference (for example, the quantitative analysis result obtained by performing a parallel test on a sample with a known state).
  • the relative abundance of these microorganisms in the intestinal flora can also be determined to determine the response effect of the subject to PD-1 antibody blockers, as well as the monitoring of non-small cell lung cancer patients. treatment effect.
  • biomarker combination refers to a combination of two or more biomarkers.
  • species markers and functional markers those skilled in the art can also determine whether the species and functions exist in the intestinal flora through conventional bacterial species identification methods and biological activity testing methods.
  • bacterial species identification can be performed by 16s rRNA.
  • the device as shown in FIG. 1 includes a sample collection device 100, a marker relative abundance determination device 200, and a response result determination device 300, the marker relative abundance determination device is connected to the sample collection device, The response result determination device is connected to the marker relative abundance determination device.
  • the sample collection device is suitable for collecting samples from patients with non-small cell lung cancer; the marker relative abundance determining device is suitable for detecting the sample to determine the relative abundance information of the markers in the sample; the response result determining device is suitable for transferring the sample Compare the relative abundance information of the markers with reference data sets or reference values to predict the therapeutic effect of PD-1 antibody blockers on patients.
  • the reference data set includes the relative abundance information and the number of the markers in a plurality of samples from patients with non-small cell lung cancer that show an effective response to the PD-1 antibody blocker. Information on the relative abundance of the marker in a sample of non-small cell lung cancer patients that showed an ineffective response to the PD-1 antibody blocker.
  • the device for determining relative abundance of markers includes: a nucleic acid sample separation unit 210, a sequencing unit 220, and a comparison unit 230.
  • the sequencing unit is connected to the nucleic acid sample separation unit, and the comparison unit is connected to the nucleic acid sample separation unit.
  • the sequencing unit is connected; wherein the nucleic acid sample separation unit is adapted to separate the nucleic acid sample from the sample; the sequencing unit constructs a sequencing library based on the nucleic acid sample, and the sequencing obtains the sequencing result; the comparison unit is adapted to perform the sequencing result Analysis to determine the relative abundance information of the markers in the sample.
  • NSCLC non-small-cell lung cancer
  • Type obtained by analyzing the obtained data.
  • Sampling time points include the baseline period before treatment and multiple time points after treatment, and regular efficacy evaluations are performed until the disease worsens (progressive disease, PD).
  • the baseline period refers to the 2 weeks before the first PD-1 blocker treatment in the trial. During this period, the patient needs to stop the previous treatment plan and control the intake of other drugs.
  • the fecal DNA of the patient was extracted using next-generation sequencing for metagenomic sequencing.
  • the species difference analysis of the intestinal flora is carried out to find out the species closely related to the curative effect.
  • Permutational multivariate analysis of variance (PERMANOVA, Permutational multivariate analysis of variance) method is used to globally determine whether there is a significant difference in bacterial group scores between different groups.
  • the permutation multivariate analysis of variance is a non-parametric multivariate statistical test. It is used in the association analysis of this project to test the composition of the intestinal flora of the samples belonging to various phenotype groups, and the information on the composition of the intestinal flora used It is often transformed by bray distance.
  • machine learning methods are used to construct a model of immunotherapy efficacy using multiple factors such as the host and flora related to the efficacy of the treatment, so as to guide the clinical precise medication.
  • This process mainly uses the random forest prediction model (Random Forest model).
  • This model is an ensemble learning method and is often used in application scenarios such as classification and regression.
  • this method will repeatedly randomly generate multi-layer decision trees to form a classification decision set, and evaluate the prediction effect of the set.
  • the random forest model in this project also integrates a cross-validation module to stabilize the training effect.
  • the model obtained by this method can not only be applied to the prediction of related curative effects, but also can prompt the bacteria markers that play a related role in the treatment.
  • the method of using the random forest model and the ROC curve is well known in the art, and those skilled in the art can set and adjust the parameters according to the specific situation. According to the embodiment of the present invention, it can be based on the literature (Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, Brown M, Floegel a., Dietrich S, Rolandsson O, Wedge DC, Goodacre R, Forouhi NG ,Sharp SJ,Spranger J,Wareham NJ,Boeing H:Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2-Diabetes Melitus in a Prospective, Nested Case Control Study.Clin 487-Study, Milik 497, 61ich-Study.
  • Example 1 The treatment response rate of PD-1 blocking inhibitors in the Chinese lung cancer patient population
  • This example includes a total of 84 patients with stage III non-small-cell lung cancer (NSCLC) who meet the requirements of the test (ie, patients who are ineffective through other treatments and are in stage III non-small cell lung cancer).
  • NSCLC stage III non-small-cell lung cancer
  • the patient Before starting PD-1 blocker immunotherapy, the patient first entered the two-week baseline management period, during which other tumor treatment options were suspended. Subsequently, the patient received PD-1 blocker once every two weeks, and received relevant examinations to determine the progress of the disease. Among them, three PD-1 blockers are randomly administered. Each patient with non-small cell lung cancer is given a specific PD-1 blocker. The three PD-1 blockers are Pembrolizumab (Pembrolizumab). ), Nivolumab and Camrelizumab.
  • Table 1 The overall response rate of Chinese lung cancer patients receiving PD-1 blocker therapy
  • the overall response rate of this therapy is only 15.48%, which is a low level compared to the data in European and American (Caucasian) population trials during the same period (ranging from 12.8% to 43.7%) (Weiping et al. 2016) .
  • the intestines of patients with non-small cell lung cancer are screened for bacteria related to the efficacy of PD-1 blocking inhibitors, and the efficacy prediction model is constructed to verify the effectiveness in independent lung cancer patients. .
  • Example 2 Screening of strains highly related to the efficacy of PD-1 blocking inhibitors in the intestines of Chinese lung cancer patients before treatment
  • Example 1 33 had taken antibiotics during the treatment period or had patients with known cancer driver genes such as ALK, EGFR, etc. positive. Considering that antibiotics will affect the results of the flora, and cancer driven Genes may affect the identification of the correlation of the flora, so the samples of these 33 people were not analyzed. Therefore, 51 human intestinal fecal samples were selected for analysis in this example. Through the analysis of these samples, the intestines of lung cancer patients before treatment are screened for bacteria that are highly correlated with the efficacy of PD-1 blocking inhibitors.
  • sample DNA extraction All stool samples were collected from the patient within a week before the start of treatment, and passed through standard procedures such as DNA extraction, high-depth sequencing, database comparison and quantification, and finally a data set of relative abundance of intestinal flora of each sample (sample DNA extraction, The procedures for sequencing, database comparison and quantification are the same as those in the literature, Fang, C., et al. Assessment of the cPAS-based BGISEQ-500 platform for metadata sequencing. Gigascience 7, 1-8 (2018). It is incorporated by reference This article).
  • the data set is subjected to a statistical test of species differences between groups (Wilcoxon rank-sum test), and finally get 17 kinds of relatively reliable differential strain markers (Table 2).
  • the median of the invalid group and the median of the effective group in Table 2 are used to indicate the relative abundance value of the invalid group of bacteria and the effective group of bacteria, that is, the relative content of each bacteria obtained by the MGS method.
  • the sum of the relative abundance values of each bacteria is 1.
  • the R (effective) shown in the column of the enrichment group means that the content of the bacteria in the effective group is significantly higher than that of the ineffective group
  • NR (invalid) indicates that the content of the bacteria in the ineffective group is significantly higher than that in the effective group.
  • the MGS (metagenomic species, metagenomic species) method is referenced from Nielsen, HBet al. Identification and assembly of genes and genetic elements without using reference genes.
  • Example 3 Screening of strains in the intestines of Chinese lung cancer patients that have potential markers for the efficacy of PD-1 blocking inhibitors before treatment and 1 to 3 months after treatment
  • Example 2 For the 51 patients in Example 2, during the treatment process, some patients gave up the treatment because their condition deteriorated, and some withdrew from the study because of other cancer side effects. These were no way to continue to provide stool samples. In order to obtain a more comprehensive sample, this embodiment screened the following samples to obtain bacterial species that have potential markers for the efficacy of PD-1 blocking inhibitors in the intestine of lung cancer patients 1 to 3 months before treatment.
  • This example includes the intestines of 51 patients before treatment, patients after 1 month of continuous treatment (34 cases in total), patients after 2 months of continuous treatment (18 cases in total), and patients after 3 months of continuous treatment (11 cases in total).
  • the subset of each treatment period in the data set is tested for differences in species statistics (Wilcoxon rank- sum test) to obtain the markers of different bacterial species. Since the degree of patient tracking coordination decreases after treatment, in order to increase the number of samples to ensure the reliability of the test, in this embodiment, the union of the patients after treatment is also taken out for testing, so as to screen for intestinal bacterial species with different efficacy after treatment. In addition, in order to select as many potential markers as possible, the statistical test results of two strategies including 0 value and eliminating 0 value were carried out (Table 3).
  • the samples collected by patients after treatment were combined. And each patient selects a sample (for example, a sample of one month after treatment, a sample of two months of treatment, or a sample of three months of treatment) as a sample representing the status of the patient's flora after treatment. These samples are combined after treatment to perform the above-mentioned tests, which can increase the sample size and make the test results more reliable.
  • Example 4 The process of constructing a curative effect prediction model through screening of potential flora markers
  • Example 2 The samples of 10 patients who responded and the samples of 10 patients who did not respond were randomly selected, and the therapeutic effect prediction model was constructed through the screening of potential flora markers obtained in Example 2 and Example 3.
  • This embodiment contains a data set of relative abundance of the microflora before treatment of 10 responsive patients and 10 non-responsive patients randomly selected from the above 51 samples.
  • This data set uses the significantly different bacteria appearing in Table 2 and Table 3 as the main potential markers as the training set to perform 100,000 cross-validation random forest screening.
  • the lowest error rate was obtained in the decision tree combination of the specific 5 intestinal bacteria combination (Table 4) (as shown in a in Figure 3).
  • This combination was used to construct a random forest-based decision model, and an area under the curve of 0.97 was obtained (the maximum value is 1), indicating that the sensitivity and specificity of the model are quite high (Figure 3 b, and Figure 3) c).
  • Annotation rate the percentage of genes clustered under this MGS number to the annotated species.
  • Table 5 The relative abundance of markers in the training set and the prediction probability of the model
  • Example 5 Effectiveness verification of curative effect prediction model in independent lung cancer patient population
  • This example contains the relative abundance data set of the flora of 14 responding patients and 19 non-responding patients out of the above 84 samples that are independent of the training set of Example 4 before treatment.
  • the obtained model performs treatment feedback prediction and evaluation.
  • the test discriminant result is 0.70 area under the curve (the highest is 1), which illustrates the repeatability and real validity of the model (as shown in Figure 3 d and Figure 3 e).
  • the content of markers in each validation set sample and the predicted response rate given by the model are shown in Table 6. Individuals whose predicted probability is higher than 0.5 will be judged as valid, otherwise invalid.
  • the model's best discriminant threshold is 0.5 and has a better discriminative effect.
  • the specific prediction method is: use "randomForest 4.6-12package" in R version 3.3.2 for random forest model classification and regression.
  • the input includes training set data (that is, the relative abundance of selected MGS species markers in the training sample, see Table 5) and sample efficacy (response or non-response), and a validation set (the MGS species selected in the validation set For the relative abundance of markers, see Table 6).
  • the inventor uses the random forest function of the random forest package in the R software to establish classification and prediction functions to predict the verification set data, and the output is the prediction result (response probability), and the optimal classification threshold is 0.5 (if the response probability is higher than 0.5 , It is predicted to have curative effect).

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Abstract

本发明公开了肠道宏基因组在预测PD-1抗体阻断剂对于癌症的治疗效果中的用途以及用于预测PD-1抗体阻断剂对非小细胞肺癌患者治疗效果的标志物及应用。该标志物包括选自Streptococcus sanguinis，Escherichia coli，Clostridium sp. L2-50，Citrobacter freundii，Pyramidobacter piscolens，及其类似物中的至少一种。

Description

肠道宏基因组在筛选PD-1抗体阻断剂疗效方面的用途
优先权信息
无。
技术领域
本发明涉及生物检测领域，具体涉及一种肠道宏基因组在筛选PD-1抗体阻断剂疗效方面的用途。
背景技术
自2011起，针对肿瘤免疫逃脱机制(Hanahan D et al,2011)的研究进程，使得对免疫检查点进行阻断的抗肿瘤免疫疗法(Pardoll DM,2012)应运而生。其中，以程序性凋亡蛋白1受体及其配体(PD-1/PD-L1)为靶点的免疫治疗方法在黑色素瘤、肺癌等多种实体瘤的高加索人群中的应用取得令人瞩目的广谱抗癌效果。
程序性凋亡蛋白1受体(以下简称PD-1)多被表达于T细胞、B细胞、巨噬细胞、调节性T细胞，以及自然杀伤细胞等免疫应答相关细胞中。PD-1能够被多种配体结合，主要两类被称为PD-1配体1和PD-1配体2。通过配体结合所激活的PD-1信号通路能够抑制T细胞的增殖和免疫应答活动。因此，激活PD-1信号通路成为了肿瘤细胞逃脱抗原特异性T细胞免疫应答的主要手段。而针对该现象所研发的PD-1抗体阻断剂能够阻断该过程，以此提高人体自身抗肿瘤免疫的活性。
而针对PD-1抗体阻断剂的个体响应差异，还需要进一步改进，来实现有区别的用药。
发明内容
本发明旨在至少在一定程度上解决相关技术中的技术问题之一，提供了一种将肠道宏基因组用于筛选PD-1抗体阻断剂疗效的方法。应用肠道宏基因组预测PD-1抗体阻断剂对于癌症的治疗效果，可以指导临床用药，从而实现个体的精准化治疗。
本发明的发明人在研究过程中发现：尽管阻断免疫检查点的免疫治疗在多种恶性肿瘤中取得令人瞩目的治疗效果，但针对不同癌种及同一癌种不同患者个体间，免疫治疗有效率波动存在较大的个体差异，响应率从12.8％到43.7％不等。而且同一PD-1抗体阻断剂在即使相同肿瘤中的治疗响应率也不同，总体响应率较低，仅在15％左右，目前还缺乏用药前对病人接受该种疗法的响应程度的有效预测方法。
为此，本发明通过筛选确定能够有效辅助PD-1抗体阻断剂对特定肿瘤病人治疗效果的菌群标志物；同时通过肠道菌种标志物的相对含量构建模型，有效预测病人接受该种疗法的预期响应程度。
根据本发明的第一方面，本发明提供了肠道宏基因组在预测PD-1抗体阻断剂对于癌症的治疗效果中的用途。宏基因组(metagenome)，也可以称作环境微生物基因组或元基因组，是特定环境中全部遗传基因的总和。肠道宏基因组(gut metagenome)，顾名思义，是指肠道环境中全部遗传基因的总和。通过对肠道宏基因组进行分析，可以预测PD-1抗体阻断剂对于癌症的治疗效果，从而是可以指导临床用药，实现精确到个体的精准治疗。
根据本发明的实施例，以上所述肠道宏基因组在预测PD-1抗体阻断剂对于癌症的治疗效果中的用途可以进一步包括如下技术特征：
在一些实施例中，利用宏基因组学分析所述肠道宏基因组的组分或者功能变化，来预测PD-1抗体阻断剂对于癌症的治疗效果。其中，宏基因组学(metagenomics)是指以环境样品中的微生物群体基因组为研究对象，通过现代基因组技术手段包括功能基因的筛选和测序分析，对环境中微生物多样性、种群结构、进化关系、功能活性、相互协作关系以及环境之间的关系进行研究的新的微生物研究方法。
在一些实施例中，所述宏基因组学为通过DNA测序，以鉴定环境样品中的微生物并定量其丰富度为数据基础的组学研究。在一些实施例中，所述宏基因组学为全基因测序宏基因组学。全基因测序宏基因组学(whole genome metagenomics)是将特定环境中的所有微生物作为一个整体，对其中的所有微生物进行测序。利用全基因测序宏基因组学可以研究样品中的功能基因或者功能微生物以及功能基因或者功 能微生物在环境中所起的作用。在一些实施例中，所述宏基因组学为16S/18S rDNA测序、或者探针测序。16S rDNA是编码原核生物核糖体小亚基16S rRNA的基因，其长度通常包括几个高变区和与之相间的保守区。高变区的变异程度与原核微生物的系统发育密切相关。通过高通量测序技术对16S rDNA高变区进行测序，可以分析环境中原核微生物的群落结构的多样性。18S rDNA是编码真核生物核糖体小亚基18S rRNA的基因，同样具有保守区和高变区。通过高通量测序技术对18S rDNA高变区进行测序，分析环境中真核微生物的菌落结构多样性。探针测序是通过设计探针捕获目标区域，例如捕获肠道宏基因组中微生物的全外显子区域，用来分析研究这些区域同疾病等的关联。
在一些实施例中，所述肠道宏基因组包括肠道微生物。肠道宏基因组包括肠道中全部遗传物质。这些遗传物质很大一部分来自于肠道微生物。通过对肠道微生物进行分析，通过肠道微生物的类型或者含量的变化，可以用来预测PD-1抗体阻断剂对于癌症的治疗效果，指导临床用药。
在一些实施例中，所述肠道宏基因组的组分变化包括肠道微生物的类型或者含量的变化。
在一些实施例中，所述肠道宏基因组的功能变化包括代谢功能基因的变化。代谢功能基因是指与肠道代谢功能相关的基因，这些基因可以表现为增强肠道的代谢功能，也可以表现为降低肠道的代谢功能。例如肠道代谢功能的改变，可以表现为肠道中微生物环境的改变，从而表现出对于PD-1抗体阻断剂响应效率的差异。
在一些实施例中，所述癌症包括选自非小细胞肺癌、结直肠癌、肝癌、食管癌、肺癌、乳腺癌、胃癌中的至少一种。这些癌症通常表现为恶性肿瘤，在利用PD-1抗体阻断剂治疗的过程中，在不同患者中通常表现出不同的响应率。如在2016年发表的一篇综述(Zou,W,et al,2016)显示，2015年之前开展的22项涉及肾癌、非小细胞肺癌以及结直肠癌使用PD-1阻断剂进行治疗的试验中，响应率的浮动范围从12.8％到43.7％不等。通过对患者的肠道基因组进行分析，可以预测PD-1抗体阻断剂对于这些癌症的治疗效果，从而指导临床用药。
在一些实施例中，所述PD-1抗体阻断剂包括选自派姆单抗(pembrolizumab)，纳武单抗(nivolumab)，Camrelizumab中的至少一种。
根据本发明的第二方面，本发明提供了一种标志物，包括选自下列中的至少一种：Streptococcus sanguinis(血链球菌)和/或其类似物，Escherichia coli(大肠杆菌)和/或其类似物，Clostridium sp.L2-50(梭菌属sp.L2-50)和/或其类似物、Citrobacter freundii(弗氏柠檬酸杆菌)和/或其类似物，Pyramidobacter piscolens(锥形杆菌属piscolens菌)和/或其类似物，所述Streptococcus sanguinis类似物与Streptococcus sanguinis的基因组序列相比，比对相似度在85％以上，所述Escherichia coli类似物与Escherichia coli的基因组序列相比，比对相似度在85％以上，所述Clostridium sp.L2-50类似物与Clostridium sp.L2-50的基因组序列相比，比对相似度在85％以上，所述Citrobacter freundii类似物与Citrobacter freundii的基因组序列相比，比对相似度在85％以上，所述Pyramidobacter piscolens类似物与Pyramidobacter piscolens的基因组序列相比，比对相似度在85％以上。
本领域技术人员可知的是，当某种未知微生物或者某种核酸来源的基因序列与某种已知菌株相比，比对相似度在95％以上的时候，即可认为该微生物与该菌株同种，或者可以将基因序列归类到与该菌株同种。由此，本领域技术人员可以直接通过将检测对象中的核酸序列信息获取，然后将其与Streptococcus sanguinis，或者与Escherichia coli，或者与Clostridium sp.L2-50，或者与Citrobacter freundii，或者与Pyramidobacter piscolens的基因组序列进行比对，如有95％以上的序列相似性，则就可以作为标志物。
根据本发明的实施例，当所述各菌类似物与相应的菌的基因组序列相比，比对覆盖度在80％以上，且比对相似度在85％以上时，均可以认为这些类似物与相应菌属于同一属，可以作为标志物。在至少一些实施例中，当这些类似物与相应的菌的比对覆盖度在80％以上，且比对相似度在95％以上时，均可以认为这些类似物与相应菌同种，可以作为标志物。
本发明中比对覆盖度，指的是在将目标序列与参考序列比对的过程中，目标序列中拿来和参考序列进行比对的序列的长度占检测序列总长度的比例。
根据本发明的第三方面，本发明提供了一种制剂，所述制剂由包含有选自Escherichia coli(大肠杆菌)和/或其类似物，Citrobacter freundii(弗氏柠檬酸杆菌)和/或其类似物中的至少一种制备而成。各标志物 菌种能够通过分离培养，与辅料配合，或者直接将相应的菌种冻干形成冻干粉，获得制剂，对非小细胞肺癌患者的病人的肠道进行干预和调理，达到提升PD-1免疫疗法对病人疗效的效果。
在至少一些实施例中，所述制剂包括选自片剂、丸剂、散剂、注射剂、溶液剂、混悬剂、胶囊剂、颗粒剂中的至少一种。
根据本发明的第四方面，本发明提供了一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法，包括：(1)从所述患者中采集样本；(2)对所述样本进行检测，以便确定所述样本中标志物的相对丰度信息，所述标志物为根据本发明第二方面所述的标志物；(3)将所述样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测PD-1抗体阻断剂对所述患者的治疗效果。优选地，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
本发明中所述参考数据集指的是对已表现出对PD-1抗体阻断剂有效响应的非小细胞肺癌患者和已表现出对PD-1抗体阻断剂无效响应的非小细胞肺癌患者样本进行操作，所获得的各标志物的相对丰度信息，用来作为每种标志物的相对丰度的参考。在本发明的一个实施方案中，参考数据集是指训练数据集。根据本发明，所述训练集是指和验证集具有本领域公知的含义。在本发明的一个实施方案中，所述训练集是指包含一定样本数的表现出对PD-1抗体阻断剂有效响应的非小细胞肺癌受试者和表现出对PD-1抗体阻断剂无效响应的非小细胞肺癌受试者待测样本中的各标志物的含量的数据集合。所述验证集是用来测试训练集性能的独立数据集合。
本发明中所述参考值指的是对PD-1抗体阻断剂有效响应的非小细胞肺癌患者中各标志物的含量的参考值或正常值。本领域技术人员已知，当对PD-1抗体阻断剂有效响应的非小细胞肺癌患者样本容量足够大时，可利用本领域公知的检测和计算方法获得样品中每个标志物的绝对值的范围。当采用测定方法检测标志物的水平时，可将样品中的标志物水平的测定值直接与参考值进行比较，以评估非小细胞肺癌患者对PD-1抗体阻断剂的响应效果。例如可以借助于统计方法来进行分析比较。
在一些实施例中，以上所述预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法还可以进一步包括如下技术特征：
在一些实施例中，在将所述样本中标志物的相对丰度信息与参考数据集进行比较时，还包括执行多元统计模型以获得响应概率。
在一些实施例中，所述多元统计模型为随机森林模型。
在一些实施例中，所述响应概率大于预定阈值表明所述PD-1抗体阻断剂能够有效治疗所述患者的非小细胞肺癌。
在一些实施例中，所述预定阈值为0.5。
在一些实施例中，步骤(2)中所述标志物的相对丰度信息是利用测序方法得到的，进一步包括：从所述患者的所述样本中分离得到核酸样本；基于所述核酸样本，构建测序文库，测序获得测序结果；对所述测序结果进行分析，以便确定所述样本中标志物的相对丰度信息。对测序结果可以利用SOAP2或者MAQ等进行比对分析，从而可以提高比对的效率，进而可以提高PD-1抗体阻断剂响应检测的效率。
在一些实施例中，所述样本为粪便样本。
在一些实施例中，所述测序是通过第二代测序方法或者第三代测序方法进行的，优选地，所述测序是通过选自Hiseq2000、SOLiD、454和单分子测序装置的至少一种进行的。利用这些测序装置的高通量、深度测序的特点，有利于对后续测序数据进行分析，尤其是进行统计学检验时的精确性和准确度。
在一些实施例中，所述PD-1抗体阻断剂包括选自派姆单抗(pembrolizumab)，纳武单抗(nivolumab)，Camrelizumab中的至少一种。
根据本发明的第五方面，本发明提供了一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的设备，包括：样本采集装置，所述样本采集装置适于从所述患者中采集样本；标志物相对丰度确定装置，所述标志物相对丰度确定装置与所述样本采集装置相连，所述标志物相对丰度确定装置适于对所述样本进行检测，以便确定所述样本中标志物的相对丰度信息，所述标志物为包含本发明第二方面所述的标志 物；结果确定装置，所述结果确定装置与所述标志物相对丰度确定装置相连，所述结果确定装置适于将所述样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测PD-1抗体阻断剂对所述患者的治疗效果；其中，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
在本发明的一些实施例中，以上所述预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的设备，可以进一步包括如下技术特征：
在一些实施例中，所述设备中，在将所述样本中标志物的相对丰度信息与参考数据集进行比较时，还包括执行多元统计模型以获得响应概率。
在一些实施例中，所述设备中，所述多元统计模型为随机森林模型。
在一些实施例中，所述设备中，所述响应概率大于预定阈值表明所述PD-1抗体阻断剂能够有效治疗所述患者的非小细胞肺癌；优选地，所述预定阈值为0.5。
在一些实施例中，所述标志物相对丰度确定装置进一步包括：核酸样本分离单元，所述核酸样本分离单元适于从所述样本中分离得到核酸样本；测序单元，所述测序单元与所述核酸样本分离单元相连，所述测序单元基于所述核酸样本，构建测序文库，测序获得测序结果；比对单元，所述比对单元适于对所述测序结果进行分析，以便确定所述样本中标志物的相对丰度信息。
在一些实施例中，所述样本为粪便样本。
在一些实施例中，所述测序通过第二代测序方法或第三代测序方法进行的；优选地，所述测序是通过选自Hiseq2000、SOLiD、454和单分子测序装置的至少一种进行的。
根据本发明的第六方面，本发明提供了一种试剂盒，包括用于检测本发明第二方面所述标志物的试剂。
根据本发明的实施例，以上所述的试剂盒可以进一步包括如下技术特征：
在一些实施例中，所述试剂盒包括至少一组参考数据集或者参考值，用来作为每种标志物的相对丰度的参考。例如可以将参考数据集或者参考值附在一些如光盘、或者CD-ROM等的物理载体上。
在一些实施例中，所述试剂盒还包括第一计算机程序产品，该第一计算机程序产品用来执行获得所述的参考数据集或者参考值。即该第一计算机程序产品用来执行获得诊断对象对于PD-1抗体阻断剂的治疗效果。
在一些实施例中，所述试剂盒还包括第二计算机程序产品，所述第二计算机程序产品用来执行本发明第四方面任一实施例所述的预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法。
根据本发明的第七方面，本发明提供了一种筛选用于非小细胞肺癌的PD-1抗体阻断剂的方法，包括：将患有非小细胞肺癌的受试者分别给予候选PD-1抗体阻断剂治疗后，测定来自所述受试者的样本中标志物的相对丰度信息，所述标志物为根据本发明第二方面所述的标志物；将治疗后的样品中所述标志物的相对丰度信息与向所述受试者施用所述候选PD-1抗体阻断剂之前的所述标志物的相对丰度信息进行比较。通过对多个患有非小细胞肺癌的受试者给予候选PD-1抗体阻断剂治疗，通过检测受试者体内各标志物的相对丰度信息的变化，可以用来指示候选PD-1抗体阻断剂对于非小细胞肺癌的效果。尤其是当多个非小细胞肺癌患者给予候选PD-1抗体阻断剂治疗后，检测确定本发明所提供的这些标志物中的至少一种在治疗前后表现出显著差异，就可以将这些候选PD-1抗体阻断剂作为治疗非小细胞肺癌的药物。当然为了实现精准化治疗，这些筛选出来的治疗非小细胞肺癌的药物在应用治疗非小细胞肺癌患者时，可以按照本发明第四方面所述的方法来预测这些筛选出来的治疗非小细胞肺癌的药物对于非小细胞肺癌患者的治疗效果。
在一些实施例中，所述Escherichia coli和/或其类似物、Citrobacter freundii和/或其类似物中的至少一种标志物的相对丰度增加表明所述候选PD-1抗体阻断剂是用于治疗所述受试者的非小细胞肺癌的药物，或者所述方法对受试者的非小细胞肺癌有效。
在一些实施例中，所述Streptococcus sanguinis和/或其类似物、Clostridium sp.L2-50和/或其类似物、Pyramidobacter piscolens和/或其类似物中的至少一种标志物的相对丰度减少表明所述候选PD-1抗体阻断 剂是用于治疗所述受试者的非小细胞肺癌的药物，或者所述方法对受试者的非小细胞肺癌有效。
在一些实施例中，所述受试者为动物或者人。
根据本发明的第八方面，本发明提供了一种标志物在制备试剂盒中的用途，所述试剂盒用于预测非小细胞肺癌患者对于PD-1抗体阻断剂的响应效果或筛选用于非小细胞肺癌的PD-1抗体阻断剂，所述标志物为本发明第二方面所述的标志物。
根据本发明的第九方面，本发明提供了一种建立用于预测非小细胞肺癌患者对于PD-1抗体阻断剂的响应效果的预测模型的方法，所述方法包括鉴定非小细胞肺癌患者对PD-1抗体阻断剂表现出有效响应与对PD-1抗体阻断剂表现出无效响应的样品之间差异表达的物质的步骤，其中所述差异表达的物质包括选自本发明第二方面所述的标志物的一种或多种。
本发明所取得的有益效果为：首次成功利用肠道微生物筛选PD-1免疫阻断剂治疗非小细胞肺癌的疗效，通过本发明所提供的肠道菌或其任意组合，能够较为理想地预测病人接受特定PD-1阻断剂免疫疗法的响应率。从而可以通过对肠道宏基因组分析，预测PD-1抗体阻断剂对于癌症的治疗效果，实现不同个体的精准医疗。
本发明的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到。
附图说明
本发明的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1是根据本发明的一个实施例提供的预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的设备的示意图。
图2是根据本发明的一个实施例提供的标志物相对丰度确定装置的示意图。
图3为根据本发明的一个实施例提供的随机森林筛选、模型构建与验证过程的示意图。其中图3中a为利用不同数量的标志物进行随机森林筛选，获得的错误变异系数；图3中b为训练集中，5种标志物组合在无效组和有效组预测有效的概率；图3中c为利用5种标志物组合和随机森林的决策模型，所获得的ROC曲线图；图3中d为验证集中，5种标志物组合在无效组和有效组预测有效的概率；图3中e为利用5种标志物组合和随机森林的决策模型，在验证集中进行验证，所获得的ROC曲线图。
具体实施方式
下面详细描述本发明的实施例，所述实施例的示例在附图中示出。下面通过参考附图描述的实施例是示例性的，旨在用于解释本发明，而不能理解为对本发明的限制。
本文中，所用术语具有相关领域普通技术人员通常理解的含义。然而，为了更好地理解本发明，对于一些定义和相关术语的解释如下：
“非小细胞肺癌”是一种恶性程度较高，易复发、转移的恶性肿瘤，包括鳞状细胞癌、腺癌、大细胞癌等。肺癌作为中国发病率和死亡率最高的恶性肿瘤，而其85％患者均属于非小细胞肺癌。已有研究显示，包括肺癌在内的多种实体瘤可以通过促进免疫抑制性细胞亚群的分化和活化来逃逸免疫系统的攻击，而肿瘤免疫治疗则可以通过激活免疫系统获得客观应答从而控制病情。近年来，以PD-1/PD-L1为靶点的抗体药物在黑色素瘤、非小细胞肺癌(NSCLC)等多种实体瘤中的应用取得令人瞩目的广谱抗癌效果。
“PD-1抗体阻断剂”也称为“PD-1阻断剂”或者“PD-1阻断抑制剂”指的是能够阻断PD-1信号通路的物质。这些物质被制备成药物，能够阻断PD-1信号通路，从而避免肿瘤细胞逃脱抗原特异性T细胞免疫应答。已经发现的PD-1抗体阻断剂包括但不限于：pembrolizumab，nivolumab，Camrelizumab(今年已经陆续在国内上市了，最后一种为国产研发的阻断剂)。
为了在前期能够辅助判断非小细胞肺癌病人对于PD-1免疫疗法的适用性，得到精确到个体的精准干预方案的参考目的，本发明提供了一种标志物，包括选自下列中的至少一种：Streptococcus sanguinis和/ 或其类似物，Escherichia coli和/或其类似物，Clostridium sp.L2-50和/或其类似物、Citrobacter freundii和/或其类似物，Pyramidobacter piscolens和/或其类似物。
其中，Streptococcus sanguinis是血链球菌，一种革兰氏阳性兼性厌氧球菌细菌，常见于人体口腔环境。
Escherichia coli是大肠杆菌，常见于动物和人的肠道内，属于革兰氏阴性兼性厌氧菌。
Clostridium sp.L2-50是尚未被命名的菌，归属梭菌属，该属广泛分布于自然界。
Citrobacter freundii是弗氏柠檬酸杆菌，属于兼性厌氧革兰氏阴性菌，是一种在健康人肠道中比较常见的菌。
Pyramidobacter piscolens是近年来从人口腔中新发现的菌。该属的菌大多为厌氧、非运动性杆菌，其最终次级代谢物以硫化氢为主。
术语“标志物”，也称为“标记物”，也可以称为“生物学标志物”或“生物学标记物”，是指个体的生物状态的可测量指标。这样的生物标志物可以是在个体中的任何物质，只要它们与被检个体的特定生物状态(例如，疾病)有关系，例如，核酸标志物(也可以称为基因标志物，例如DNA)，蛋白质标志物，细胞因子标志物，趋化因子标志物，碳水化合物标志物，抗原标志物，抗体标志物，物种标志物(种/属的标志)和功能标志物(KO/OG标志)等。其中，核酸标志物的含义并不局限于现有可以表达为具有生物活性的蛋白质的基因，还包括任何核酸片段，可以为DNA，也可以为RNA，可以是经过修饰的DNA或者RNA，也可以是未经修改的DNA或者RNA，以及由它们组成的集合。在本文中核酸标志物有时也可以称为特征片段。在本发明中，标志物也可以用“肠道标志物”来替代，因为本发明所发现的与PD-1抗体阻断剂响应效果密切相关的几种生物标志物均存在于受试者的肠道内。生物标志物经过测量和评估，经常用以检查正常生物过程，致病过程，或治疗干预药理响应，而且在许多科学领域都是有用的。
利用如上标志物，本发明提供了一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法，包括：(1)从所述患者中采集样本；(2)对所述样本进行检测，以便确定所述样本中标志物的相对丰度信息；(3)将所述样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测PD-1抗体阻断剂对所述患者的治疗效果；其中，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
根据本发明的实施例，可以运用高通量测序，批量分析对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者的粪便样本和对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的粪便样本。基于高通量测序数据，对由对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者群样本和对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者群样本进行比对，从而确定与PD-1抗体阻断剂响应效果密切相关的特异性核酸序列。简言之，其步骤如下：
样品的收集与处理：收集对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者的粪便样本与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的粪便样本，使用试剂盒进行DNA提取，得到核酸样本；
文库构建和测序：DNA文库构建和测序是利用高通量测序进行，以便得到粪便样品中所包含肠道微生物的核酸序列；
通过生物信息学的分析方法，确定与PD-1抗体阻断剂响应效果相关的特异性肠道微生物核酸序列。首先，将测序序列(reads)与参照基因集(也称为参考基因集，可以为新构建的基因集或任何已知序列的数据库，例如，采用已知的人肠道微生物群落非冗余基因集)进行比对。接下来，基于比对结果，分别确定来自对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者粪便样品的核酸样本中各基因的相对丰度。通过将测序序列与参照基因集进行比对，可以将测序序列与参照基因集中的基因建立对应关系，从而针对核酸样本中的特定基因，与其相对应的测序序列的数目可以有效地反映该基因的相对丰度。由此，可以通过比对结果，按照常规的统计分析，确定在核酸样本中基因的相对丰度。最后，在确定核酸样本中各基因的相对丰度后，对来自对PD-1 抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的核酸样本中各基因的相对丰度进行统计检验，由此，可以判断对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者中是否存在相对丰度有显著差异的基因，如果存在基因是显著差异的，则该基因被当作是异常状态的生物标志物，即核酸标志物。
另外，对于已知或新构建的参照基因集，其通常包含基因物种信息和功能注释，由此，在确定基因相对丰度的基础上，可以进一步通过将基因的物种信息和功能注释进行分类，从而确定肠道菌群中各微生物的物种相对丰度和功能相对丰度，也就可以进一步确定异常状态的物种标志物和功能标志物。简言之，确定物种标志物和功能标志物的方法进一步包括：将对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的测序序列与参照基因集进行比对；基于比对结果，分别确定对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的核酸样本中各基因的物种相对丰度和功能相对丰度；对来自对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的核酸样本中各基因的物种相对丰度和功能相对丰度进行统计学检验；以及分别确定在对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的核酸样本之间相对丰度存在显著差异的物种标志物和功能标志物。根据本发明的实施例，可以采用对来自相同物种的基因的相对丰度和具有相同功能注释的基因的相对丰度进行统计检验，例如加和、取平均值、中位数值等，来确定功能相对丰度和物种相对丰度。
最后，确定了对PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者与对PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者的粪便样品之间相对丰度存在显著差异的生物学标志物，即包括微生物物种：Streptococcus sanguinis和/或其类似物，Escherichia coli和/或其类似物，Clostridium sp.L2-50和/或其类似物，Citrobacter freundii和/或其类似物，Pyramidobacter piscolens和/或其类似物，由此，通过检测上述微生物至少一种是否存在，来有效地确定对象对于PD-1抗体阻断剂的响应效果，并且可以用于监控非小细胞肺癌患者的治疗效果。在本文中所使用的术语“存在”应做广义理解，既可以指的是定性分析样本中是否含有相应的目标物，也可以指对样本中的目标物进行定量分析，并且还可以进一步将所得到的定量分析结果与参照(例如通过对具有已知状态的样本进行平行试验所得到的定量分析结果)进行统计学分析或者任何已知数学运算所得到的结果。本领域技术人员可以根据需要和试验条件进行容易的选择。根据本发明的实施例，还可以通过确定这些微生物在肠道菌群中的相对丰度，从而能够确定对象对于PD-1抗体阻断剂的响应效果，以及用于监控非小细胞肺癌患者的治疗效果。
可以通过检测对象肠道菌群中是否存在上述微生物物种中的至少一种，也可以是检测对象肠道菌群中是否存在上述中的两种或者多种，即是否存在上述生物标志物组合，从而来有效地确定对象对于PD-1抗体阻断剂的响应效果，并且可以用于监控非小细胞肺癌患者的治疗效果。在本文中，术语“生物标志物组合”是指由两个或更多个生物标志物组成的组合。
对于物种标志物和功能标志物本领域技术人员还可以通过常规的菌种鉴别手段和生物活性检验手段来确定在肠道菌群中是否存在所述物种和功能。例如，菌种鉴别可以通过进行16s rRNA进行。
另外，根据本发明的另一些实施例，提供了一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的设备。在至少一个实施例中，所述设备如图1所示，包括样本采集装置100、标志物相对丰度确定装置200和响应结果确定装置300，标志物相对丰度确定装置与样本采集装置相连，响应结果确定装置与标志物相对丰度确定装置相连。其中样本采集装置适于从非小细胞肺癌患者中采集样本；标志物相对丰度确定装置适于对样本进行检测，以便确定样本中标志物的相对丰度信息；响应结果确定装置适于将样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测PD-1抗体阻断剂对患者的治疗效果。其中，在至少一个实施方式中，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
在至少一个实施方式中，标志物相对丰度确定装置如图2所示，包括：核酸样本分离单元210、测 序单元220和比对单元230，测序单元与核酸样本分离单元相连，比对单元与测序单元相连；其中，核酸样本分离单元适于从所述样本中分离得到核酸样本；测序单元基于所述核酸样本，构建测序文库，测序获得测序结果；比对单元适于对所述测序结果进行分析，以便确定所述样本中标志物的相对丰度信息。
下面将结合实施例对本发明的方案进行解释。本领域技术人员将会理解，下面的实施例仅用于说明本发明，而不应视为限定本发明的范围。实施例中未注明具体技术或条件的，按照本领域内的文献所描述的技术或条件或者按照产品说明书进行。所用试剂或仪器未注明生产厂商者，均为可以通过市购获得的常规产品。
本实验通过收集符合PD-1抗体III期临床试验入组条件的非小细胞肺癌(Non-small-cell lung carcinoma，以下称NSCLC)患者，采集受试者治疗前后的血液和粪便样本及临床表型，对所得数据进行分析所获得的。采样时间点包括治疗前基线期和治疗后多个时间点并定期进行疗效评价，直至疾病恶化(progressive disease,PD)。基线期是指病人在试验中首次服用PD-1阻断剂治疗前2周的时间。该期间病人需停止先前的治疗方案，并控制其他药物的摄入。
提取患者粪便DNA采用二代测序进行宏基因组测序。结合多元统计与数据挖掘方法，进行肠道菌群的物种差异分析，找出与疗效密切相关的物种。利用置换多元方差分析(PERMANOVA，Permutational multivariate analysis of variance)方法在全局上判断菌群组分是否在不同的分组之间存在显著性差异。其中置换多元方差分析方法属于一种非参数多元统计检验，在本项目关联分析中用于对各类表型分组所属样本在肠道菌群的组成进行检验，所采用的肠道菌群组成信息往往经过bray距离转化。
然后根据患者在治疗前的肠道微生物组成，结合临床表型信息，利用机器学习方法，构建利用与疗效相关的宿主、菌群等多元因素进行免疫治疗疗效的模型，从而指导临床精准用药。
该过程主要利用了随机森林预测模型(Random Forest model)。该模型属于集成学习方法，常用于分类、回归等应用场景。在模型训练阶段，该方法将反复随机生成多层决策树，构成一个分类决策集合，并对该集合的预测效果进行评估。为了避免过拟合的现象，本项目中的随机森林模型还整合了交叉验证模块，用以稳定训练效果。该方法获得的模型不仅可应用于相关疗效的预测，还能提示对该种治疗方式起到关联作用的菌种标志物。
其中，随机森林模型和ROC曲线的使用方法为本领域所公知，本领域技术人员可以根据具体情况进行参数设置和调整。根据本发明的实施例，可以根据文献(Drogan D,Dunn WB,Lin W,Buijsse B,Schulze MB,Langenberg C,Brown M,Floegel a.,Dietrich S,Rolandsson O,Wedge DC,Goodacre R,Forouhi NG,Sharp SJ,Spranger J,Wareham NJ,Boeing H:Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2-Diabetes Mellitus in a Prospective,Nested Case Control Study.Clin Chem 2015,61:487-497.；Mihalik SJ,Michaliszyn SF,de las Heras J,Bacha F,Lee S,Chace DH,DeJesus VR,Vockley J,Arslanian SA:Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2diabetes:evidence for enhanced mitochondrial oxidation.Diabetes Care 2012,35:605-611.，通过引用全文并入此处)中记载的方法进行。
实施例1：PD-1阻断抑制剂在中国肺癌患者群体中的治疗响应率
本实施例包含符合试验要求(即经其他方式治疗无效且处于非小细胞肺癌III期的病人)的III期非小细胞肺癌(Non-small-cell lung carcinoma，以下称NSCLC)患者共计84人。患者在开始接受PD-1阻断剂免疫疗法前，首先进入为期两周的基线期管理阶段，该阶段内暂停其他肿瘤治疗方案。随后，患者每两周接受一次PD-1阻断剂服用，并接受相关检查判断病情发展情况。其中采用三种PD-1阻断剂随机给药，每个非小细胞肺癌患者给予一种特定的PD-1阻断剂，这三种PD-1阻断剂分别为派姆单抗(Pembrolizumab)、纳武单抗(Nivolumab)和Camrelizumab。
根据实体瘤响应评价标准(RECIST)1.1，患者在接受治疗三个月后的响应率分布情况如表1所示。
表1中国肺癌病人接受PD-1阻断剂疗法的整体响应率
响应评价 病情恶化 病情稳定 部分缓解
男 26 17 8
女 22 6 5
总体 48 23 13
比例 57.14％ 27.38％ 15.48％
如表1所示，该疗法的整体响应率仅有15.48％，相比于同期欧美(高加索)人群试验中的数据(12.8％到43.7％不等)属于较低水平(Weiping et al.2016)。
其中，在治疗过程中，通过如下实施例，筛选非小细胞肺癌患者肠道中与PD-1阻断抑制剂疗效相关的菌种，并构建疗效预测模型，在独立肺癌患者群体中进行有效性验证。
实施例2：筛选治疗前中国肺癌患者肠道中与PD-1阻断抑制剂疗效高度相关的菌种
实施例1中的84人中，有33人在治疗期间服用过抗生素或者有查出有ALK、EGFR等已知癌症驱动基因阳性的患者，考虑到抗生素会影响到菌群的结果，而癌症驱动基因可能会影响到对于菌群的相关性的鉴别，因此并未对这33人的样本进行分析。因此本实施例选择了51例人肠道粪便样品进行分析。通过对这些样品进行分析，筛查治疗前肺癌患者肠道中与PD-1阻断抑制剂疗效高度相关的菌种。
所有粪便样品采集自患者在治疗开始之前一周内，并经过DNA提取，高深度测序，数据库比对与定量等标准流程，最后获得各个样本的肠道菌群相对丰度数据集(样品DNA提取、测序、数据库比对和定量等流程与文献相同，Fang,C.,et al.Assessment of the cPAS-based BGISEQ-500platform for metagenomic sequencing.Gigascience 7,1-8(2018).通过参照将其并入本文)。以病人在治疗三个月后是否恶化为分组(病情恶化为无效组，病情稳定、部分缓解为有效组)，对该数据集进行组间差异物种统计检验(Wilcoxon rank-sum test)，最终得到17种相对可靠的差异菌种标志物(表格2)。
对于表1中示出的病人的三种情况：病情恶化的，属于无效组；部分缓解的，属于有效组；病情稳定的，对于稳定期超过3个月的，也属于有效组。这种病情恶化、部分缓解以及病情稳定的判断是由临床医生通过观察病人的反应，在必要时进行CT后判断得到的。
其中，表2中无效组中位数和有效组中位数用来表示无效组菌种和有效组菌种的相对丰度值，即通过MGS方法进行定量所得到的每一种菌的相对含量，各菌的相对丰度值的和为1。富集组一列所显示的R(有效)代表该菌在有效组的含量显著高于无效组，NR(无效)代表该菌在无效组的含量显著高于有效组。其中MGS(metagenomic species,宏基因组物种)方法，参考自Nielsen,H.B.et al.Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.Nature biotechnology 32,822-828,doi:10.1038/nbt.2939(2014)。无论是表2中列出在有效组富集的菌种，还是在无效组富集的菌种，这些与PD-1阻断抑制剂疗效相关的显著差异菌种均可以作为潜在的菌种标志物，作为后续能够用于预测PD-1阻断抑制剂对于非小细胞肺癌患者的治疗效果的候选标志物。
表2治疗前中国肺癌患者肠道中与PD-1阻断抑制剂疗效的显著差异菌种
Figure PCTCN2019075825-appb-000001
Figure PCTCN2019075825-appb-000002
实施例3：筛选治疗前和治疗后1到3个月中国肺癌患者肠道中与PD-1阻断抑制剂疗效具有潜在标志物可能的菌种
对于实施例2中的51例病人，在治疗过程中有些患者因为病情恶化而放弃治疗，还有一些因为其他癌症副作用而退出研究，这些都没有办法继续提供粪便样本。为了获得更全面的样本，所以本实施例针对如下样本进行了筛选，得到治疗前1到3个月肺癌患者肠道中与PD-1阻断抑制剂疗效具有潜在标志物可能的菌种。
本实施例包含治疗前51例患者、连续治疗1个月后患者(共计34例)、连续治疗2个月后患者(共计18例)、连续治疗3个月后患者(共计11例)的肠道粪便样品。所有肠道粪便样品经过与实施例2完全一致的样品DNA提取、测序、数据库比对和定量等流程获得各个样本的肠道菌群相对丰度数据集。以病人在治疗三个月后是否恶化为分组(病情恶化为无效组，病情稳定、部分缓解为有效组)，对该数据集中各个治疗时期的子集进行组间差异物种统计检验(Wilcoxon rank-sum test)，得到差异菌种标志物。由于治疗后病人跟踪配合度降低，为了提升样本数以保证检验的可靠性，本实施例中还将治疗后病人的并集取出进行检验，以筛选治疗后对疗效具有差异的肠道菌种。此外，为了尽可能多地选择潜在的标志物，分别进行了包含0值和剔除0值两种策略的统计检验结果(表3)。
针对表3中的策略一栏，即包含0值以及剔除0值，做以下说明：在研究过程中，同样的菌，在有些病人中有，有些病人中没有，对于在病人中没有的菌，则认为其丰度值为0。而基于丰度值为0的不同原因，有两种不同的策略。一种是认为丰度值为0，代表病人没有这种菌，该病人样本应该放进去进行统计检验；而另一种策略是认为丰度值为0，是由于检测手段精度不够而检测不到所致，并非代表病人样品中不含有该菌，在这种情况下，就要将0值排除以免干扰。我们对两种策略均进行了检测，以获得尽可能多的潜在标志物。
同时，由于无法强制要求病人严格按照试验时间点提供样品，因遗忘、无便意、条件不允许等情况导致样本无法采集的情况客观存在，导致有些病人样本没有被包含在治疗后的组里。为了提高样本量，将治疗后的病人采集的样本进行了合并。且每个病人挑出一个样本(例如可以是治疗后一个月的样本，也可以是治疗两个月的样本，或者是治疗三个月的样本)作为代表该病人治疗后菌群状态的样本。这些样本组成治疗后并集，进行上述检测，这样可以提高样本量，使检验结果的可信度更高。
无论是表3中列出的在有效组富集的菌种，还是在无效组富集的菌种，这些与PD-1阻断抑制剂疗效相关的显著差异菌种均可以作为潜在的菌种标志物，作为后续能够用于预测PD-1阻断抑制剂对于非小细胞肺癌患者的治疗效果的候选标志物。
表3治疗前和治疗后1到3个月肺癌患者肠道中与PD-1阻断抑制剂疗效的显著差异菌种
Figure PCTCN2019075825-appb-000003
Figure PCTCN2019075825-appb-000004
Figure PCTCN2019075825-appb-000005
Figure PCTCN2019075825-appb-000006
Figure PCTCN2019075825-appb-000007
Figure PCTCN2019075825-appb-000008
Figure PCTCN2019075825-appb-000009
实施例4：通过潜在菌群标志物筛选构建疗效预测模型的过程
随机选择10例有响应的病人的样品以及10例无响应的病人的样品，通过实施例2和实施例3得到的潜在菌群标志物筛选构建疗效预测模型。
本实施例包含从上述51例样本中随机筛选获得的10例有响应病人和10例无响应病人在治疗前的菌群相对丰度数据集。该数据集以上述表2和表3中出现的显著差异菌为主要潜在标志物作为训练集进行100000次交叉验证的随机森林筛选。经过随机森林模型筛选验证，在特定的5种肠道菌组合(表4)的决策树组合中获得了最低的错误率(如图3中a所示)。该组合被用于构建基于随机森林的决策模型，并获得了0.97的曲线下面积(最大值为1)，说明该模型的敏感性与特异性相当高(如图3中b，以及图3中c所示)。
表4随机森林模型肠道菌中标志物组合
Figure PCTCN2019075825-appb-000010
注释率：聚类到该MGS编号下的基因占所注释物种的百分比。
每个训练样本标志物含量及模型给出预测响应率见表5。预测概率高于0.5的个体将判为有效，反之无效。
表5训练集标志物相对丰度与模型预测概率表
Figure PCTCN2019075825-appb-000011
实施例5：疗效预测模型在独立肺癌患者群体中的有效性验证
本实施例包含上述84例样本中独立于实施例4训练集以外的14例有响应病人和19例无响应病人在治疗前的菌群相对丰度数据集，作为验证集，应用实施例4中获得的模型进行治疗反馈预测并做评价。 测试判别结果得到0.70的曲线下面积(最高为1)，说明了该模型的可重复性和真实有效性(如图3中d，以及图3中e所示)。每个验证集样本标志物含量及模型给出预测响应率见表6。预测概率高于0.5的个体将判为有效，反之无效，模型最佳判别阈值在0.5时具有较好的判别效果。
具体预测方法为：在3.3.2版本R中使用“randomForest 4.6-12package”进行随机森林模型分类和回归。输入包括训练集数据(即训练样本中选定的MGS物种标志物的相对丰度，见表5)和样本疗效情况(有响应或无响应)，以及一个验证集(验证集中所选属MGS物种标志物的相对丰度，见表6)。然后，发明人利用R软件中随机森林包的随机森林函数建立分类和预测函数对验证集数据进行预测，输出即为预测结果(响应概率)，最佳分类阈值为0.5(如果响应概率高于0.5，则预测有疗效)。
表6验证集标志物相对丰度与模型预测概率表
Figure PCTCN2019075825-appb-000012
Figure PCTCN2019075825-appb-000013
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
尽管上面已经示出和描述了本发明的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本发明的限制，本领域的普通技术人员在本发明的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (33)

  1. 肠道宏基因组在预测PD-1抗体阻断剂对于癌症的治疗效果中的用途。
  2. 根据权利要求1所述的用途，其特征在于，利用宏基因组学分析所述肠道宏基因组的组分或者功能变化，来预测PD-1抗体阻断剂对于癌症的治疗效果。
  3. 根据权利要求2所述的用途，其特征在于，所述宏基因组学为全基因组测序、16S/18S rDNA测序、或者探针测序。
  4. 根据权利要求1所述的用途，其特征在于，所述肠道宏基因组包括肠道微生物。
  5. 根据权利要求1所述的用途，其特征在于，所述肠道宏基因组的组分变化包括肠道微生物的类型或者含量的变化。
  6. 根据权利要求1所述的用途，其特征在于，所述肠道宏基因组的功能变化包括代谢功能基因的变化。
  7. 根据权利要求1所述的用途，其特征在于，所述癌症包括选自非小细胞肺癌、结直肠癌、肝癌、食管癌、肺癌、乳腺癌、胃癌中的至少一种。
  8. 根据权利要求1所述的用途，其特征在于，所述PD-1抗体阻断剂包括选自派姆单抗(pembrolizumab)，纳武单抗(nivolumab)，Camrelizumab中的至少一种。
  9. 一种标志物，其特征在于，包括选自下列中的至少一种：
    Streptococcus sanguinis(血链球菌)和/或其类似物，Escherichia coli(大肠杆菌)和/或其类似物，Clostridium sp.L2-50和/或其类似物，Citrobacter freundii(弗氏柠檬酸杆菌)和/或其类似物，Pyramidobacter piscolens和/或其类似物，
    其中，所述Streptococcus sanguinis类似物与Streptococcus sanguinis的基因组序列相比，比对相似度在85％以上，
    所述Escherichia coli类似物与Escherichia coli的基因组序列相比，比对相似度在85％以上，
    所述Clostridium sp.L2-50类似物与Clostridium sp.L2-50的基因组序列相比，比对相似度在85％以上，
    所述Citrobacter freundii类似物与Citrobacter freundii的基因组序列相比，比对相似度在85％以上，
    所述Pyramidobacter piscolens类似物与Pyramidobacter piscolens的基因组序列相比，比对相似度在85％以上。
  10. 一种制剂，其特征在于，所述制剂由包含有选自Escherichia coli(大肠杆菌)和/或其类似物，Citrobacter freundii(弗氏柠檬酸杆菌)和/或其类似物中的至少一种制备而成。
  11. 根据权利要求10所述的制剂，其特征在于，所述制剂包括选自片剂、丸剂、散剂、注射剂、溶液剂、混悬剂、胶囊剂、颗粒剂中的至少一种。
  12. 一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法，其特征在于，包括：
    (1)从所述患者中采集样本；
    (2)对所述样本进行检测，以便确定所述样本中标志物的相对丰度信息，所述标志物为权利要求9所述的标志物；
    (3)将所述样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测PD-1抗体阻断剂对所述患者的治疗效果；
    优选地，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
  13. 根据权利要求12所述的方法，其特征在于，在将所述样本中标志物的相对丰度信息与参考数据集进行比较时，还包括执行多元统计模型以获得响应概率。
  14. 根据权利要求13所述的方法，其特征在于，所述多元统计模型为随机森林模型。
  15. 根据权利要求13所述的方法，其特征在于，所述响应概率大于预定阈值表明所述PD-1抗体阻 断剂能够有效治疗所述患者的非小细胞肺癌。
  16. 根据权利要求15所述的方法，其特征在于，所述预定阈值为0.5。
  17. 根据权利要求12所述的方法，其特征在于，步骤(2)中所述标志物的相对丰度信息是利用测序方法得到的，进一步包括：
    从所述患者的所述样本中分离得到核酸样本；
    基于所述核酸样本，构建测序文库，测序获得测序结果；
    对所述测序结果进行分析，以便确定所述样本中标志物的相对丰度信息。
  18. 根据权利要求17所述的方法，其特征在于，所述样本为粪便样本。
  19. 根据权利要求17所述的方法，其特征在于，所述测序是通过第二代测序方法或第三代测序方法进行的；优选地，所述测序是通过选自Hiseq2000、SOLiD、454、和单分子测序装置的至少一种进行的。
  20. 根据权利要求12所述的方法，其特征在于，所述PD-1抗体阻断剂包括选自派姆单抗(pembrolizumab)，纳武单抗(nivolumab)，Camrelizumab中的至少一种。
  21. 一种预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的设备，其特征在于，包括：
    样本采集装置，所述样本采集装置适于从所述患者中采集样本；
    标志物相对丰度确定装置，所述标志物相对丰度确定装置与所述样本采集装置相连，所述标志物相对丰度确定装置适于对所述样本进行检测，以便确定所述样本中标志物的相对丰度信息，所述标志物为权利要求9所述的标志物；
    响应结果确定装置，所述响应结果确定装置与所述标志物相对丰度确定装置相连，所述响应结果确定装置适于将所述样本中标志物的相对丰度信息与参考数据集或参考值进行比较，预测所述PD-1抗体阻断剂对所述患者的治疗效果；
    其中，所述参考数据集包括多个对所述PD-1抗体阻断剂表现出有效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息和多个对所述PD-1抗体阻断剂表现出无效响应的非小细胞肺癌患者样本中所述标志物的相对丰度信息。
  22. 根据权利要求21所述的设备，其特征在于，在将所述样本中标志物的相对丰度信息与参考数据集进行比较时，还包括执行多元统计模型以获得响应概率。
  23. 根据权利要求22所述的设备，其特征在于，所述多元统计模型为随机森林模型；优选地，所述响应概率大于预定阈值表明所述PD-1抗体阻断剂能够有效治疗所述患者的非小细胞肺癌；优选地，所述预定阈值为0.5。
  24. 根据权利要求21所述的设备，其特征在于，所述标志物相对丰度确定装置包括：
    核酸样本分离单元，所述核酸样本分离单元适于从所述样本中分离得到核酸样本；
    测序单元，所述测序单元与所述核酸样本分离单元相连，所述测序单元基于所述核酸样本，构建测序文库，测序获得测序结果；
    比对单元，所述比对单元与所述测序单元相连，所述比对单元适于对所述测序结果进行分析，以便确定所述样本中标志物的相对丰度信息。
  25. 根据权利要求24所述的设备，其特征在于，所述测序是通过第二代测序方法或第三代测序方法进行的；优选地，所述测序是通过选自Hiseq2000、SOLiD、454和单分子测序装置的至少一种进行的。
  26. 一种试剂盒，其特征在于，包括用于检测权利要求9所述的标志物的试剂。
  27. 根据权利要求26所述的试剂盒，其特征在于，所述试剂盒包括至少一组参考数据集或者参考值，所述参考数据集或者参考值用来作为每种标志物的相对丰度的参考。
  28. 根据权利要求27所述的试剂盒，其特征在于，所述试剂盒还包括第一计算机程序产品，所述第一计算机程序产品用来执行获得所述的参考数据集或者参考值。
  29. 根据权利要求26所述的试剂盒，其特征在于，所述试剂盒还包括第二计算机程序产品，所述第二计算机程序产品用来执行权利要求12～20中任一项所述的预测PD-1抗体阻断剂对非小细胞肺癌患者的治疗效果的方法。
  30. 一种筛选用于非小细胞肺癌的PD-1抗体阻断剂的方法，其特征在于，包括：
    将患有非小细胞肺癌的受试者分别给予候选PD-1抗体阻断剂治疗后，测定来自所述受试者的样品中根据权利要求9所述的标志物的相对丰度信息；
    将治疗后的样品中所述标志物的相对丰度信息与向所述受试者施用所述候选PD-1抗体阻断剂之前的所述标志物的相对丰度信息进行比较；
    任选地，所述Escherichia coli和/或其类似物、Citrobacter freundii和/或其类似物中的至少一种标志物的相对丰度增加表明所述候选PD-1抗体阻断剂是用于治疗所述受试者的非小细胞肺癌的药物，或者所述方法对受试者的非小细胞肺癌有效；
    任选地，所述Streptococcus sanguinis和/或其类似物、Clostridium sp.L2-50和/或其类似物、Pyramidobacter piscolens和/或其类似物中的至少一种标志物的相对丰度减少表明所述候选PD-1抗体阻断剂是用于治疗所述受试者的非小细胞肺癌的药物，或者所述方法对受试者的非小细胞肺癌有效。
  31. 根据权利要求30所述的筛选用于非小细胞肺癌的PD-1抗体阻断剂的方法，其特征在于，所述受试者为动物或者人。
  32. 标志物在制备试剂盒中的用途，其特征在于，所述试剂盒用于预测非小细胞肺癌患者对于PD-1抗体阻断剂的响应效果或筛选用于非小细胞肺癌的PD-1抗体阻断剂，所述标志物为权利要求9所述的标志物。
  33. 一种建立用于预测非小细胞肺癌患者对于PD-1抗体阻断剂的响应效果的预测模型的方法，所述方法包括鉴定非小细胞肺癌患者对PD-1抗体阻断剂表现出有效响应与对PD-1抗体阻断剂表现出无效响应的样品之间差异表达的物质的步骤，其中所述差异表达的物质包括选自权利要求9所述的标志物的一种或多种。
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