CN112251512B - Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit - Google Patents

Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit Download PDF

Info

Publication number
CN112251512B
CN112251512B CN202011335077.6A CN202011335077A CN112251512B CN 112251512 B CN112251512 B CN 112251512B CN 202011335077 A CN202011335077 A CN 202011335077A CN 112251512 B CN112251512 B CN 112251512B
Authority
CN
China
Prior art keywords
lung cancer
small cell
cell lung
mutation
gene
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011335077.6A
Other languages
Chinese (zh)
Other versions
CN112251512A (en
Inventor
王凯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Origimed Technology Shanghai Co ltd
Original Assignee
Origimed Technology Shanghai Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Origimed Technology Shanghai Co ltd filed Critical Origimed Technology Shanghai Co ltd
Priority to CN202011335077.6A priority Critical patent/CN112251512B/en
Publication of CN112251512A publication Critical patent/CN112251512A/en
Application granted granted Critical
Publication of CN112251512B publication Critical patent/CN112251512B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis
    • 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
    • C12Q2600/156—Polymorphic or mutational markers

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Genetics & Genomics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Physics & Mathematics (AREA)
  • Organic Chemistry (AREA)
  • Pathology (AREA)
  • Zoology (AREA)
  • Biophysics (AREA)
  • Immunology (AREA)
  • Biotechnology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Wood Science & Technology (AREA)
  • Microbiology (AREA)
  • Biochemistry (AREA)
  • Oncology (AREA)
  • General Engineering & Computer Science (AREA)
  • Hospice & Palliative Care (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Medical Informatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The invention provides a target genome for gene detection of a patient with non-small cell lung cancer, and a related evaluation method, application and kit, wherein the target genome is used for evaluating whether the patient with non-small cell lung cancer has the necessity of carrying out tumor mutation load detection, and the target genome comprises the following genes: EGFR, TP53, CDKN2A, and SDHA.

Description

Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit
Technical Field
The invention belongs to the field of biological information, and particularly relates to a target genome for gene detection of a patient with non-small cell lung cancer, a related evaluation method, a related application and a related kit.
Background
The emergence of Next Generation Sequencing (NGS) technology has brought a new era for the precise treatment of cancer. NGS is capable of detecting hundreds of common cancer-associated genes on a single chip, and is not only the first choice for targeted therapies based on Tyrosine Kinase Inhibitors (TKIs), but also indispensable for immune therapies based on Immune Checkpoint Inhibitors (ICI).
Although targeted therapy for the first time demonstrated the effectiveness of precise drugs and small molecule inhibitors against key driver mutations (e.g., EML4-ALK fusions or EGFR L858R mutations) in non-small cell lung cancer (NSCLC) and demonstrated significant success, resistance often occurred within months and adverse effects may be very severe. In recent years, immunotherapy, which destroys cancer cells by activating and activating the human body's own immune system, has shown great promise and has attracted increasing attention. However, despite the significant efficacy of immunotherapy, its applicability is limited, with only 10% -20% of patients responding to most cancer types.
In different cancer types, tumor Mutational Burden (TMB) has become a powerful biomarker for the success of immunotherapy. This reflects the competition of many molecular switches, and TMB has a clear rationale, but a complex mechanism. The downstream is closely related to neoantigen burden (NAB), affecting immunogenicity. Upstream, it may have multiple drivers, from genetic factors (such as HRD, dMMR or extreme mutations) to environmental factors (such as smoking or excessive sun exposure). Thus, the attractiveness of TMB as a biomarker lies not only in its theoretical reliability, but also in its broad coverage.
Therefore, depending on the size of the tumor mutation load, it can be used in all cancers to select populations that would benefit from high benefit of treatment, to improve the economics of immunotherapy, and to avoid unnecessary waste of resources.
However, since tumor mutation burden is statistical mutation of all genes in cancer tissue, it is necessary to perform genome-wide sequencing on cancer tissue, which is expensive, even if FDA-approved detection is performed only by covering all exons plus all introns of key genes (i.e., large panel method including a plurality of key genes), because of the large number of genes, the price is still high, patients are difficult to bear, and the time for performing genome-wide or large panel detection is also long, it is easy for patients whose immunotherapy benefit is not good to miss the optimal treatment time of other treatment protocols, so it is obvious that direct tumor mutation burden detection to evaluate the immunotherapy benefit of patients cannot meet the actual situation in application.
Disclosure of Invention
The invention provides a target genome for gene detection of a patient with non-small cell lung cancer, and an evaluation method for tumor mutation load detection necessity and an evaluation method for immunotherapy benefit based on the target genome.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a target genome for gene detection of a patient with non-small cell lung cancer, which is characterized in that: the target genome is used for evaluating whether a non-small cell lung cancer patient has the necessity of carrying out tumor mutation load detection, wherein the target genome comprises the following genes: EGFR, TP53, CDKN2A, and SDHA.
The target genome provided by the invention also has the following characteristics: wherein the necessity of assessing the tumor mutational burden detection of a non-small cell lung cancer patient is non-essential when the target genome of a sample from the non-small cell lung cancer patient is judged to satisfy any one or more of the following three predetermined conditions: the first predetermined condition is: TP53 gene mutation, concurrent with EGFR gene mutation; the second predetermined condition is: CDKN2A gene mutation, concurrent with EGFR gene mutation; the third predetermined condition: the TP53 gene and the SDHA gene do not mutate simultaneously.
The target genome provided by the invention also has the following characteristics: wherein the necessity of assessing a tumor mutational burden detection of a non-small cell lung cancer patient is not non-essential when a target genome of a sample from the non-small cell lung cancer patient is judged not to satisfy any one of three predetermined conditions.
The target genome provided by the invention also has the following characteristics: wherein, the mutation related to each gene is any one or more mutation sites as shown in the table 2.
The invention also provides an evaluation method for detecting the necessity of tumor mutation load, which is used for evaluating whether a patient with non-small cell lung cancer has the necessity of tumor mutation load detection, and is characterized in that: assessment of the necessity of tumor mutation burden detection based on a target genome comprising the following genes: EGFR, TP53, CDKN2A, and SDHA.
The evaluation method provided by the invention also has the following characteristics: wherein the necessity of assessing the tumor mutation burden detection of a non-small cell lung cancer patient is non-essential when the target genome of a sample from the non-small cell lung cancer patient is judged to satisfy any one of the following three predetermined conditions: the first predetermined condition is: TP53 gene mutation, concurrent with EGFR gene mutation; the second predetermined condition is: CDKN2A gene mutation, concurrent with EGFR gene mutation; the third predetermined condition: the TP53 gene and the SDHA gene are not mutated at the same time.
The evaluation method provided by the invention also has the following characteristics: wherein the necessity of assessing a tumor mutational burden detection of a non-small cell lung cancer patient is not non-essential when a target genome of a sample from the non-small cell lung cancer patient is judged not to satisfy any one of three predetermined conditions.
The evaluation method provided by the invention also has the following characteristics: wherein, the mutation related to each gene comprises the mutation site shown in the table 2.
The invention also provides an evaluation method of the benefit of immunotherapy, which is used for evaluating the benefit of immunotherapy on a patient with non-small cell lung cancer, and is characterized by comprising the following steps: step 1, assessing the necessity of tumor mutation load detection, and assessing the necessity of tumor mutation load detection of a patient with non-small cell lung cancer based on a target genome; step 2, detecting the tumor mutation load, and detecting the tumor mutation load of a non-small cell lung cancer patient of which the tumor mutation load detection is not unnecessary to evaluate to obtain the tumor mutation load; and 3, evaluating the benefit of immunotherapy on the non-small cell lung cancer patient of which the tumor mutation load is considered to be unnecessary by evaluating in the step 1 or on the non-small cell lung cancer patient of which the tumor mutation load is obtained in the step 2, wherein the target genome comprises the following genes: EGFR, TP53, CDKN2A, and SDHA.
The invention also provides the use of a target genome in the preparation of a product for assessing the necessity of detecting the tumor mutation load of a patient with non-small cell lung cancer, which is characterized in that: wherein the target genome is the target genome described above.
The invention also provides a kit for detecting a target genome necessary for detecting tumor mutation load of a patient with non-small cell lung cancer, which is characterized in that: the target genome is the target genome described above.
Action and Effect of the invention
According to the target genome for gene detection of the non-small cell lung cancer patient and the related evaluation method, application and kit, the target genome comprises genes EGFR, TP53, CDKN2A and SDHA, and the necessity of tumor mutation load detection of the non-small cell lung cancer patient can be evaluated according to the relevance analysis of the TMB value results of a sample from the non-small cell lung cancer patient, so that whether the non-small cell lung cancer patient easily benefits from immunotherapy or not, namely the benefit of the immunotherapy can be evaluated based on the target genome with extremely small number of the included genes, and the tumor mutation load detection necessity can be evaluated instead of directly detecting the tumor mutation load, so that the cost can be low and the initial judgment can be carried out in a short time compared with the evaluation of directly detecting the tumor mutation load, and on one hand, the tumor mutation load detection necessity evaluation result is not further carried out on the non-small cell lung cancer patient who is not necessary; on the other hand, it is possible to avoid unnecessary detection of tumor mutation burden in a non-small cell lung cancer patient whose evaluation result of the necessity of tumor mutation burden detection is unnecessary, thereby avoiding unnecessary economic burden on the part of non-small cell lung cancer patients and avoiding delaying the optimal time for other treatment protocols due to the detection of tumor mutation burden.
Drawings
Fig. 1 is a flowchart of a method for evaluating the benefit of immunotherapy according to example 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention refers to the accompanying drawings. For the specific methods or materials used in the embodiments, those skilled in the art can make routine alternatives based on the existing technologies based on the technical idea of the present invention, and the alternatives are not limited to the specific descriptions of the embodiments of the present invention.
The methods used in the examples are conventional methods unless otherwise specified; the materials, reagents and the like used are commercially available unless otherwise specified.
Example 1
In this example 1, the solid tumor is taken as a sample to be tested, and the tumor mutation load is predicted as an example.
Fig. 1 is a flowchart of a method for evaluating the benefit of immunotherapy according to example 1 of the present invention.
As shown in fig. 1, this example 1 provides a method for evaluating the benefit of immunotherapy, which includes the following steps:
step 1 (S1), assessing the necessity of tumor mutation burden detection, specifically:
assessment of the necessity of a non-small cell lung cancer (NSCLC) patient tumor mutation burden detection based on the genome of interest.
The selected target genome comprises the genes and the corresponding descriptions shown in table 1.
Figure GDA0003884240250000071
Designing corresponding probes aiming at a target genome, then carrying out capture sequencing on a sample from a non-small cell lung cancer patient based on the designed probes to obtain a sequencing result, then comparing the sequencing result with a reference genome to obtain corresponding comparison information, and judging according to the comparison information, namely judging whether the target genome of the sample meets any one of the following three preset conditions or not, so as to evaluate the tumor mutation load detection necessity of the non-small cell lung cancer patient, specifically:
the three predetermined conditions are:
the first predetermined condition is: TP53 gene mutation, concurrent with EGFR gene mutation;
the second predetermined condition is: CDKN2A gene mutation, concurrent with EGFR gene mutation;
the third predetermined condition: the TP53 gene and the SDHA gene are not mutated at the same time.
When the target genome is judged to meet any one or more of the three predetermined conditions, the tumor mutation load detection necessity of the corresponding non-small cell lung cancer patient is evaluated as unnecessary, and at this time, the subsequent tumor mutation load detection is not required to be carried out, and the step 3 is directly carried out to evaluate the benefit of the immunotherapy.
Wherein, it is clear that the following conditions are all met:
(1) Only a first predetermined condition is satisfied;
(2) Only a second predetermined condition is satisfied;
(3) Only a third predetermined condition is satisfied;
(4) Only a first predetermined condition and a second predetermined condition are satisfied;
(5) Only the first predetermined condition and the third predetermined condition are satisfied;
(6) Only the second predetermined condition and the third predetermined condition are satisfied;
(7) The first predetermined condition, the second predetermined condition and the third predetermined condition are all satisfied.
On the contrary, when the target genome is judged not to satisfy any one of the above three predetermined conditions, that is, when the target genome of the sample is sequenced and the mutation status does not include any one of the above 7 scenarios, it is not necessary to evaluate the tumor mutation burden detection necessity of the corresponding non-small cell lung cancer patient. To further assess whether the non-small cell lung cancer patient would benefit from immunotherapy, it is recommended that the follow-up tumor mutation burden testing be resumed.
In this embodiment, specifically, in the above three predetermined conditions, the mutation related to each gene is any one or more of the mutation sites as shown in table 2.
Figure GDA0003884240250000091
Figure GDA0003884240250000101
Figure GDA0003884240250000111
Figure GDA0003884240250000121
Step 2, detecting the tumor mutation load, specifically comprising the following steps:
this step is performed on non-small cell lung cancer patients who are not necessarily evaluated for the tumor mutation burden detection in step 1.
In this step, the Tumor Mutation Burden (TMB) is detected by covering the whole exon + the key gene intron as described above, and the calculation formula of TMB is: number of mutations/length of sequencing.
The sequencing length is the size of the assay region for which sequencing is performed, and is calculated by removing overlapping portions between probes designed to capture the relevant genes in the assay region and accumulating the overlapping portions.
Step 3, evaluating the benefit of immunotherapy, which is specifically divided into two types:
(1) Non-small cell lung cancer patients considered non-essential for tumor mutational burden detection as assessed by step 1 were assessed for their benefit of immunotherapy: at this time, based on the evaluation results of step 1, it can be seen that the detection of tumor mutation load is unnecessary for the evaluation of the corresponding non-small cell lung cancer patient, and the patient is considered to have less benefit, i.e., low benefit, from the immunotherapy.
(2) And (3) evaluating the benefit of immunotherapy on the corresponding non-small cell lung cancer patient evaluated by the non-small cell lung cancer patient subjected to the tumor mutation load obtained in the step (2), wherein the evaluation is carried out based on the tumor mutation load obtained in the step (2), and when the size of the tumor mutation load exceeds a threshold value, the evaluation considers that the patient benefits from the immunotherapy more easily, namely easily benefits from the immunotherapy, and the benefit is high, otherwise, the benefit is not high.
The threshold is the size of the tumor mutation load generally accepted as being beneficial for non-small cell lung cancer, and may be, for example, 10, and when the tumor mutation load is detected and calculated to be greater than 10, the benefit is considered to be high.
As can be seen from the above, for the non-small cell lung cancer patient, in order to evaluate whether it is easy to benefit from the immunotherapy, that is, the benefit of the immunotherapy, the evaluation of the necessity of the tumor mutation load detection may be performed first, instead of directly performing the evaluation of the tumor mutation load detection, and at this time, since the target genome on which the evaluation of the tumor mutation load detection in step 1 is performed includes a very small number of genes, it is possible to take a preliminary judgment in a short time at a low cost relatively to the recent past, so that, on the one hand, the further tumor mutation load detection is not performed on the non-small cell lung cancer patient who does not satisfy any one or more of the above three predetermined conditions (that is, the non-small cell lung cancer patient whose necessity of the evaluation of the tumor load detection is not unnecessary), and, on the other hand, the unnecessary tumor mutation detection on the non-small cell lung cancer patient who satisfies any one or three of the above three predetermined conditions (that is, the necessity of the evaluation of the distillation load detection is not necessary), can be performed, thereby, the economical tumor mutation load detection can be avoided on the part, and the other hand, the delay of the optimal tumor treatment can be avoided.
Example 2
This example 2 is to demonstrate that it can be used for the evaluation of the necessity of tumor mutation burden detection and the evaluation of the benefit of immunotherapy for non-small cell lung cancer patients based on the target genome in example 1.
The sample source information on which the verification of this example 2 is based is shown in table 3.
Figure GDA0003884240250000141
In this embodiment:
in the first step, the 291 samples were sequenced and TMB detected in the manner of the large panel: constructing a sample DNA sequencing library, and performing target region capture enrichment on the library by using a specific probe, wherein the process is performed by using a kit; the captured library can realize one-time detection of multiple mutations of multiple genes through high-throughput sequencing.
Specifically, the following are:
(1) Taking DNA extracted from formalin-fixed paraffin embedded (FFPE) and blood samples (matched samples) as a material, and performing fragmentation treatment, adaptor addition, PCR enrichment and other steps on the material to prepare a pre-library; then hybridizing a pre-library with a DNA probe having a specific sequence to specifically capture exon and intron regions from the target gene in the human genome; then enriching DNA fragments captured by the probe by a magnetic bead method, and quantifying and controlling the captured library; and finally, performing high-throughput sequencing on the quantified library by using a gene sequencer. Bioinformatics software was used to determine whether there was a tumor-derived variation in the target gene. The kit comprises negative and positive quality control products and is used for monitoring random errors and systematic errors of an experiment link and a data analysis link.
(2) And (3) sequencing the tumor tissue and the matched blood sample to obtain a FASTQ format sequence, and comparing the FASTQ file with a reference sequence of a human genome to generate an alignment BAM format file if the quality control of the FASTQ file is qualified. The average depth of the sequencing data can be obtained by counting the BAM files. And (3) comparing the tissue sample with a matched blood sample, only keeping somatic cell mutation, obtaining a mutation site of a coding region, and further removing the SNP site and Driver mutation of a public database to obtain a final mutation site. These sites were counted and divided by the base size (Mb) of the coding region to obtain the number of gene variations per Mb interval as the TMB value.
The applicable instrument: nextSeq 550Dx or NovaSeq 6000 from Illumina.
Sample requirements:
the sample type is derived from a matched sample of the same individual, and the sample type is specifically as follows: blood samples preserved with EDTA blood collection tubes and Formalin Fixed Paraffin Embedded (FFPE) solid tumor tissue samples. The storage life of the FFPE sample does not exceed 2 years.
The detection method comprises the following steps:
in the FFPE and blood nucleic acid extraction stage, wax block samples are mainly sliced, and slices with high tumor content are selected for subsequent work;
constructing a library; end repairing: this step filled in the DNA ends and phosphorylated at the 5 'end and dA tails at the 3' end; connecting a joint: this step attaches a linker to the end of the product after end repair; library amplification: this step will perform PCR amplification on the purified adaptor ligation product.
Library capture: probe hybridization: mixing, re-dissolving and hybridizing the library; capturing: the DNA sequence is acquired and the captured data is then selected for amplification.
Processing on a computer and sequencing: mixing the on-machine library through a reagent; and (4) detecting whether the volume of the mixed sample library accords with the computer arrangement after mixing. Then samples are denatured, renatured (https:// www.biomart.cn/experiment/430/502/527/528/28099. Htm) diluted and the like, and pre-machine pretreatment is carried out; and then, sequencing on a computer to obtain a sequencing result.
Secondly, comparing the related key genes according to the sequencing results, wherein each two genes are in a group (A gene and B gene), the two genes are divided into the TMB value (represented by x) of all samples with A gene mutation but without B gene mutation, the TMB value (represented by y) of all samples with B gene mutation but with A gene mutation and the TMB value (represented by z) of all samples with two genes mutated, and then the significant analysis is respectively carried out between x and y, between x and z and between y and z to obtain the corresponding p values, for example, as follows: when the key genes totally have four genes, namely a, b, c and d, the three TMB values are divided into a TMB value with a gene mutation but without b gene mutation, a TMB value with b gene mutation but with a gene mutation and a TMB value with both a gene mutation and b gene mutation, and the three TMB values are subjected to significant analysis according to the mode, so that after one group is completed, the next group, such as a and c, is sequentially carried out until each gene and the other gene form one group to obtain each significant analysis result.
And thirdly, filtering the result obtained in the second step as follows: the samples corresponding to the simultaneous mutation of the two genes (double genes) to be significantly analyzed are left in a number greater than 5, so as to ensure that the data have statistical significance, i.e. 25% and 75% of the data can be evaluated: maximum, upper quartet, median, lower quartet, and minimum.
In example 2, the results of the above-described significant analysis are shown in table 4.
Figure GDA0003884240250000171
Figure GDA0003884240250000181
Table 4 illustrates:
1. each gene appearing in the table is each key gene involved in the large panel assay of example 2;
2. p _ a _ B represents the significance analysis P-value between the TMB values of all samples with a gene mutation but without B gene mutation and the TMB values of all samples with B gene mutation but without a gene mutation;
3. p _ a _ booth represents the significance analysis P-value between the TMB value of all samples with a gene mutation but no B gene mutation and the TMB value of all samples with both a and B genes mutated;
4. p _ B _ booth represents the significance analysis P-value between the TMB value of all samples with B gene mutation but without a gene mutation and the TMB value of all samples with both a and B genes mutated;
5. # A represents the median of TMB values for all samples with A gene mutation but no B gene mutation;
6. # B represents the median of TMB values for all samples with a B gene mutation but without a A gene mutation;
7. # both represents the median of TMB values for all samples in which both the A and B genes were mutated.
Thirdly, according to the results in table 4, a group in which any one of the three p values corresponding to p _ a _ B, p _ a _ both and p _ B _ both is less than or equal to 0.05 is screened out, that is, through the above significant analysis process, a pair of genes in which the TMB results are significantly correlated with each other is finally screened out, and the results in table 5 are obtained.
Figure GDA0003884240250000191
According to the corresponding genes in Table 5, the corresponding mutation sites are counted to obtain the sites shown in Table 2.
In addition, in example 2, the threshold value of TBM is 10, i.e., the non-small cell lung cancer patients with TMB values over 10 are considered to have greater benefit in immunotherapy, and vice versa.
As can be seen from Table 5, when two genes EGFR (A) and TP53 (B) are combined, the median TMB of all samples with only EGFR mutation but no TP53 mutation is the smallest (4.8), i.e., the median TMB of the samples in this case is only 4.8, which indicates that the TMB value of a patient with non-small cell lung cancer with such mutation is difficult to reach 10 with a high probability according to statistical analysis; secondly, the median TMB values (7.2) for all samples with both EGFR and TP53 mutations, and similarly, the TMB for a non-small cell lung cancer patient with such a mutation is unlikely to reach 10 with high probability; finally, the median TMB of all samples with only TP53 mutation but no EGFR mutation (13.6) demonstrated that TMB in a patient with non-small cell lung cancer with such mutations would most likely exceed 10. The results indicate that for a patient with non-small cell lung cancer, there is a correlation between the effect of EGFR and TP53 on the dual genes on the TMB value of the patient with non-small cell lung cancer, which is: when the TP53 is mutated, if EGFR mutation is also existed, the TMB value of a sample is obviously reduced, namely when both EGFR and TP53 are mutated, the TMB value result is inhibited. Therefore, when a sample of a non-small cell lung cancer patient is tested to satisfy the first predetermined condition, i.e., the mutation in the TP53 gene and the mutation in the EGFR gene, it can be estimated from the above discussion that the final tumor mutation load test result of the non-small cell lung cancer patient is less than the threshold value of 10 with a high probability.
Similarly, from table 5, it can be seen that:
there is also a correlation between EGFR and CDKN2A pairs of genes in their effect on the TMB value of the sample, which is: when both EGFR and CDKN2A are mutated, the result of TMB value is inhibited. Therefore, when a sample of a non-small cell lung cancer patient is tested to satisfy the second predetermined condition, i.e., CDKN2A3 gene mutation, and EGFR gene mutation, it can be estimated from the above discussion that the final tumor mutation burden test result of the non-small cell lung cancer patient is less than the threshold value of 10 with high probability;
there was also a correlation between SDHA and TP53 on the effect of the dual genes on the TMB value of the sample, which is: when the SDHA is mutated, if TP53 is also not mutated at the same time, the TMB value of a sample is also significantly lower and lower than the threshold 10, i.e. when the mutation is present between the SDHA and TP53 genes alone, the presence of the mutation at the same time can enhance the TBM value result to exceed the threshold 10. Therefore, when a sample from a patient with non-small cell lung cancer is tested to satisfy the third predetermined condition, i.e., SDHA and TP53 are not mutated simultaneously, it can be estimated from the above discussion that the final tumor mutation load test result of the patient with non-small cell lung cancer is less than the threshold value of 10 with a high probability.
When the TMB value of a sample is less than the threshold, it means that the non-small cell lung cancer patient does not benefit much from immunotherapy, so when one sample from the non-small cell lung cancer patient satisfies one or more of the three predetermined conditions, the necessity of performing tumor mutation load detection is unnecessary, that is, it is not necessary to continue to detect the tumor mutation load, thereby avoiding unnecessary economic burden on the non-small cell lung cancer patient, and enabling the non-small cell lung cancer patient to select other suitable therapies in time, so as to avoid delaying the optimal time of other treatment regimens.
On the contrary, when a sample of a patient with non-small cell lung cancer does not satisfy any one of the above three predetermined conditions according to the target genome detection, it is largely suggested that the necessity of tumor mutation burden detection of the patient with colorectal cancer is not unnecessary, so that the patient with non-small cell lung cancer can more reliably evaluate the self-benefit of immunotherapy according to the final tumor burden.
Therefore, from the above summary, it can be seen that the target genome in example 1 can be used for the evaluation of the necessity of tumor mutation load detection and the evaluation of the benefit of immunotherapy in non-small cell lung cancer patients.
Effects and effects of the embodiments
The target genome for gene detection of a non-small cell lung cancer patient, the method for evaluating the necessity of tumor mutation burden detection based on the target genome, and the method for evaluating the benefit of immunotherapy provided in example 1, as can be seen from example 2, since the target genome includes genes EGFR, TP53, CDKN2A, and SDHA, and the correlation analysis of the TMB value results of these genes for a sample from a non-small cell lung cancer patient can be used to evaluate the necessity of tumor mutation burden detection of a non-small cell lung cancer patient, so as to evaluate whether it is easily benefited from immunotherapy, i.e., the benefit of immunotherapy, the necessity of tumor mutation burden detection can be evaluated based on the target genome including the extremely small number of genes, rather than directly performing tumor mutation burden detection, so that it is possible to do not perform further analysis in a short time and at low cost compared with directly performing tumor mutation burden detection, and thus, on the one hand, the result of tumor mutation burden detection necessity evaluation is not further unnecessary for non-small cell lung cancer patient; on the other hand, unnecessary detection of tumor mutation load can be avoided for non-small cell lung cancer patients whose evaluation result of tumor mutation load detection necessity is unnecessary, so that unnecessary economic burden can be avoided for this part of non-small cell lung cancer patients, and delay of optimal time for other treatment protocols due to tumor mutation load detection can be avoided.

Claims (2)

1. Use of a target genome in the manufacture of a product for assessing the necessity of a non-small cell lung cancer patient for detection of tumor mutational burden, wherein the target genome comprises:
wherein the target genome is the following genes: EGFR, TP53, CDKN2A, and SDHA; the detection necessity is used for evaluating whether the non-small cell lung cancer patient has the necessity of carrying out tumor mutation load detection; assessing the necessity of detection of a tumor mutational burden in a non-small cell lung cancer patient when the target genome of a sample from the non-small cell lung cancer patient is judged to satisfy any one or more of the following three predetermined conditions:
the first predetermined condition is: TP53 gene mutation, concurrent with EGFR gene mutation;
the second predetermined condition is: CDKN2A gene mutation, concurrent with EGFR gene mutation;
the third predetermined condition: the TP53 gene and the SDHA gene are not mutated at the same time;
wherein, when the target genome of a sample from the non-small cell lung cancer patient is judged not to satisfy any one of the three predetermined conditions, it is not non-essential to evaluate the necessity of tumor mutation burden detection for the non-small cell lung cancer patient.
2. Use according to claim 1, characterized in that:
wherein, the mutation related to each gene is any one or more of the mutation sites as shown in the table 2.
CN202011335077.6A 2020-11-24 2020-11-24 Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit Active CN112251512B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011335077.6A CN112251512B (en) 2020-11-24 2020-11-24 Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011335077.6A CN112251512B (en) 2020-11-24 2020-11-24 Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit

Publications (2)

Publication Number Publication Date
CN112251512A CN112251512A (en) 2021-01-22
CN112251512B true CN112251512B (en) 2022-12-23

Family

ID=74225491

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011335077.6A Active CN112251512B (en) 2020-11-24 2020-11-24 Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit

Country Status (1)

Country Link
CN (1) CN112251512B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116355851B (en) * 2023-03-13 2023-09-08 广州医科大学附属第一医院（广州呼吸中心） Primary cell strain derived from human non-small cell lung cancer, and preparation method and application thereof

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109033749A (en) * 2018-06-29 2018-12-18 深圳裕策生物科技有限公司 A kind of Tumor mutations load testing method, device and storage medium

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109033749A (en) * 2018-06-29 2018-12-18 深圳裕策生物科技有限公司 A kind of Tumor mutations load testing method, device and storage medium

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Integration of Tumor Mutation Burden and PD-L1 Testing in Routine Laboratory Diagnostics in Non-Small Cell Lung Cancer;Stefanie Schatz 等;《Cancers》;20200624;第12卷(第1685期);第11-14页 *
晚期非小细胞肺癌患者肿瘤突变负荷与靶向治疗疗效相关性;童琳 等;《中国临床医学》;20190831;第26卷(第4期);第538-542页 *

Also Published As

Publication number Publication date
CN112251512A (en) 2021-01-22

Similar Documents

Publication Publication Date Title
JP7119014B2 (en) Systems and methods for detecting rare mutations and copy number variations
US20220090184A1 (en) Size-Selection of Cell-Free DNA for Increasing Family Size During Next-Generation Sequencing
US10704086B2 (en) Systems and methods to detect rare mutations and copy number variation
CN107475375B (en) A kind of DNA probe library, detection method and kit hybridized for microsatellite locus related to microsatellite instability
US10998084B2 (en) Sequencing data analysis method, device and computer-readable medium for microsatellite instability
CN112397144B (en) Method and device for detecting gene mutation and expression quantity
US11193175B2 (en) Normalizing tumor mutation burden
CN110628880B (en) Method for detecting gene variation by synchronously using messenger RNA and genome DNA template
JP2023526252A (en) Detection of homologous recombination repair defects
CN112251512B (en) Target genome for gene detection of non-small cell lung cancer patient and related evaluation method, application and kit
CN112259165B (en) Method and system for detecting microsatellite instability state
CN112442538B (en) Target genome for gene detection of colorectal cancer patient and related evaluation method, application and kit
EP3688195A1 (en) Biomarkers for colorectal cancer detection
CN116355987A (en) Method for producing a reference for gene mutation, reference for gene mutation and use thereof
JP2024513668A (en) Methods and related aspects for analyzing molecular responses
CN112391474A (en) Method for predicting esophageal squamous carcinoma metastasis based on fusobacterium nucleatum in tumor
CN110564851A (en) Group of genes for molecular typing of non-hyper-mutant rectal cancer and application thereof
CN112080566B (en) Thyroid cancer detection product based on high-throughput sequencing method and application
CN117660655A (en) Probe for detecting microsatellite instability, kit and application
CN117512116A (en) Biomarker for bile duct cancer detection and application thereof

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant