CN111583992B - RNA level fusion gene mutation-caused tumor load analysis system and method - Google Patents
RNA level fusion gene mutation-caused tumor load analysis system and method Download PDFInfo
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Abstract
The invention relates to a system and a method for analyzing tumor load caused by RNA level fusion gene mutation, belonging to the technical field of gene detection. The tumor burden analysis system comprises: and a data acquisition module: the method is used for acquiring off-machine data obtained by sample gene detection; the letter generation analysis module: the method is used for respectively calculating the copy number of the target fusion gene and the copy number of the reference gene; load analysis module: and substituting the target fusion gene copy number A and the reference gene copy number B into a tumor burden equation to calculate the tumor burden Z of the sample. The tumor burden analysis system can quantify the gene expression quantity of tumor genes, and prompt the content of tumor cells carrying the tumor gene variation in a certain sample, thereby providing curative effect monitoring, tumor burden, prognosis evaluation and the like and realizing accurate tumor treatment.
Description
Technical Field
The invention relates to the technical field of gene detection, in particular to a system and a method for analyzing the load of tumors caused by RNA level fusion gene mutation.
Background
The concomitant diagnosis and targeted therapy are important components for realizing accurate tumor treatment, and more valuable medical information can be provided for clinic through accurate detection of molecular markers, thereby helping to realize personalized treatment.
With the increasing complexity and comprehensiveness of the requirements for diagnostic information, an integrated detection platform (All in One) capable of detecting multiple markers simultaneously becomes a mainstream direction of the current accurate diagnosis of tumors. While the unique advantage of second generation sequencing technology (NGS) in this respect provides the possibility of achieving accurate medical treatment.
DNA and RNA analysis based on an NGS platform is an emerging liquid biopsy technology, can realize noninvasive detection of tumor whole genetic maps, has the characteristics of high sample accessibility, sustainable monitoring and the like, and has potential application value in each stage in the whole tumor treatment and management process. The analysis of genome can analyze critical oncogenic mutation early, guide treatment selection and monitor drug-resistant mutation, and the quantitative analysis of molecules by continuous liquid biopsy can provide curative effect monitoring, tumor load and prognosis evaluation.
In the conventional technology, in clinical efficacy monitoring, the change of the number of tumor cells in a patient needs to be judged according to the proportion of the molecular markers. Since the chromosomes of human cells are all 2-fold (excluding rare polyploids), the mutation ratio calculation at the DNA level is simpler, i.e
The mutant rate value can be representative of tumor burden, i.e., the proportion of tumor cells.
However, the calculation of the mutation ratio of the fusion gene at the RNA level is complicated, and it is a common practice to calculate the relative expression level of the fusion gene A by using a housekeeping gene whose expression is stable as an internal reference, namely, the following formula
Thus, the expression values of the gene A in different batches of detection before and after treatment can be compared in a horizontal line.
However, there is also a prominent problem that the relative expression level of the fusion gene A does not directly reflect the level of tumor burden.
Disclosure of Invention
In view of the above, it is necessary to provide a tumor burden analysis system by which the gene expression level of a tumor gene can be quantified, and the content of tumor cells carrying the tumor gene mutation in a sample can be indicated, not only the presence or absence of tumor cells, but also the accurate treatment of tumors can be better achieved.
A load analysis system for a tumor caused by mutation of an RNA level fusion gene, comprising:
and a data acquisition module: the method is used for acquiring off-machine data obtained by sample gene detection;
the letter generation analysis module: the method is used for respectively calculating the copy number of the target fusion gene and the copy number of the reference gene;
load analysis module: substituting the target fusion gene copy number A and the reference gene copy number B into the following tumor burden equation, and calculating to obtain a tumor burden Z of the sample;
Z＝k×B/A+a
wherein: k, a is a constant obtained by linear regression fitting;
the tumor burden equation is obtained by the following method:
taking a plurality of target fusion gene mutation positive samples as modeling samples, simultaneously obtaining pathological evaluation tumor load Z of a plurality of modeling samples, and obtaining target fusion gene copy number A and internal reference gene copy number B of the samples, wherein A and B are independent variables, Z is dependent variable, and performing linear regression analysis by a least square method to obtain a linear regression equation, namely the tumor load equation.
According to the tumor burden analysis system, a correlation and linear regression equation of the relative expression quantity R of the tumor burden Z and the fusion gene is established, and the tumor burden of the sample can be estimated by measuring the relative expression value of the specific expression A gene and the internal reference gene B. The analysis system can quantify the gene expression quantity of the tumor gene, and indicate the content of tumor cells carrying the tumor gene variation in a certain sample, rather than just indicate the existence of the tumor cells (namely negative or positive).
In one embodiment, the reference gene meets the following inclusion criteria:
1) The reference gene is not a pseudogene;
2) The internal reference gene is moderately expressed;
3) The expression of the internal reference gene between tissues is stable, and the standard deviation [ log2 (RPKM) ] of the expression value between tissues is <1;
4) The reference gene expression level is independent of cell cycle and cell activation state.
The above moderate expression means: the FPKM geometric mean of the genes encoding the proteins was arranged in ascending order, with genes between 33% and 67% positions defined as moderate expression. Specifically, the DNA sequence can be obtained according to a Human Body Map (HBM) 2.0 transcriptome sequencing data set (transcriptome of 16 organ tissues of normal people is sequenced by researchers such as Thibaut.
In one embodiment, the reference gene does not have the following exclusion conditions:
1) The reference gene has different expression in normal and abnormal cells and tissues;
2) The reference gene exhibits cell cycle dependent expression;
3) The reference gene is located on the x chromosome.
In one embodiment, the tumor is synovial sarcoma, the target fusion gene is an SS18-SSX1 fusion gene, and the reference gene is selected from the group consisting of: IPO8, JUN, LRP1, MYC, YWHAZ.
In one embodiment, the reference gene is selected from YWHAZ.
In one embodiment, the number of modeling samples is ≡6.
In one embodiment, the ratio of the sum of squares of regression to the sum of squares of total dispersion of the tumor burden equation is greater than or equal to 0.95.
In one embodiment, the pathological assessment tumor burden Z of the modeled sample is obtained by: taking a modeling sample, and evaluating the tumor content by a pathologist according to a conventional HE staining method.
In one embodiment, the tumor burden analysis system is used in liquid biopsies and blood tumors.
The invention also discloses a tumor load analysis method caused by RNA level fusion gene mutation for non-diagnostic treatment purpose, which comprises the following steps:
and (3) data acquisition: acquiring off-machine data obtained by sample gene detection;
and (5) letter generation analysis: respectively calculating the copy number of the target fusion gene and the copy number of the reference gene;
load analysis: substituting the target fusion gene copy number A and the reference gene copy number B into the following tumor burden equation, and calculating to obtain a tumor burden Z of the sample;
Z＝k×B/A+a
wherein: k, a is a constant obtained by linear regression fitting;
the tumor burden equation is obtained by the following method:
taking a plurality of target fusion gene mutation positive samples as modeling samples, simultaneously obtaining pathological evaluation tumor load Z of a plurality of modeling samples, and obtaining target fusion gene copy number A and internal reference gene copy number B of the samples, wherein A and B are independent variables, Z is dependent variable, and performing linear regression analysis by a least square method to obtain a linear regression equation, namely the tumor load equation.
Compared with the prior art, the invention has the following beneficial effects:
according to the tumor load analysis system caused by RNA level fusion gene mutation, a correlation and linear regression equation of a tumor load Z and a fusion gene relative expression quantity R (namely, the ratio of a reference gene copy number B to a target fusion gene copy number A) are established, and the tumor load of a sample can be estimated by measuring the relative expression value of a specific expression A gene and a reference gene B. The method can quantify the gene expression quantity of the tumor gene, and indicate the content of tumor cells carrying the tumor gene variation in a certain sample, rather than just indicate the existence of the tumor cells (namely negative or positive).
In addition, the tumor burden analysis system can provide curative effect monitoring, tumor burden, prognosis evaluation and the like through quantitative analysis of molecules by continuous liquid biopsy, so that accurate tumor treatment is realized.
Drawings
FIG. 1 is a linear regression fit equation for the ratio B/A of copy number of reference gene YWHAZ to copy number of fusion gene SS18-SSX1 and tumor cell content Z in example 1.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example 1
1. And (5) designing a tumor load analysis model.
Theoretically, if a certain tumor cell in the sample is X (number) and a normal cell is Y (number), the tumor burden of the sample is:
the tumor cells detect the fusion gene A, the expression of the fusion gene A is a copy, the expression quantity of the internal reference gene B is B copy, and the relative expression quantity of the fusion gene A in the sample is as follows:
substituting formula I into formula II to obtain the following formula III:
i.e., if the tumor cells stably express the fusion gene A and the reference gene B, then the tumor burden Z is linearly related to R.
The correlation of Z with R and the linear regression equation can be determined by using the actual sample data.
2. And establishing a tumor burden analysis model to obtain a tumor burden equation.
1. Obtaining the actual sample tumor content.
Using 6 synovial sarcoma tissue samples and corresponding blood samples, the tissue samples were routinely HE stained by a pathologist and assessed for tumor content (%).
The main technical links of the conventional HE dyeing tabletting comprise tissue fixation and sampling, tissue dehydration, embedding, slicing, baking, dyeing and sealing; the pathologist judges the content (%) of tumor cells in the tissue according to the specific cell morphology and distribution positions of different tumor cells. And each sample is used for diagnosing and judging at least three pathologists, the average value is taken as the content of tumor cells, and if the content values given by two of the at least three pathologists differ by more than 10%, the reorganization data are discarded and are evaluated separately.
Meanwhile, the free RNA of the plasma is extracted by adopting a BIOG plasma free RNA extraction kit, a cDNA library is constructed by adopting a Ion AmpliSeqTMLibrary Kit 2.0 kit, and the library is sequenced by a PGM sequencer.
2. And (5) selecting an internal reference gene.
The ideal reference gene is a gene that is suitable for all physiological conditions, and should meet the following conditions:
(1) Absence of pseudogenes (pseudogenes) to avoid amplification of genomic DNA;
(2) Moderate expression, excluding too high or low expression, specifically, genes between 33% and 67% positions were defined as moderate expression according to the Human Body Map (HBM) 2.0 transcriptome sequencing dataset (transcriptome obtained by sequencing normal Human 16 organ tissues by researchers such as Thibaut (Human housekeeping genes, trends in Genetics, october 2013, vol.29, no 10)), FPKM geometric mean of the encoded protein genes were arranged in ascending order;
(3) Stably expressed in different types of cells and tissues (e.g., normal cells and cancer cells), i.e., standard deviation [ log2 (RPKM) ] of expression values between tissues <1;
(4) The expression level is independent of cell cycle and whether the cells are activated or not.
And the conditions for excluding the reference gene are:
(1) There is a difference in expression in normal and abnormal cells/tissues;
(2) Presenting cell cycle dependent expression;
(3) Mapping to the x chromosome.
Studies have shown that the ideal reference gene does not exist and that the stability of expression of the reference gene is inconsistent in different treatments and in different tissues. Therefore, careful analysis and confirmation according to different experimental conditions is required to select a proper reference gene when the selection of the marker gene is performed.
The invention analyzes the characteristics of plasma samples of synovial sarcoma patients, and selects 10 housekeeping genes as reference genes: HMBS, HPRT1, IPO8, ITGB7, JUN, LRP1, MRPL13, MYC, TBP, YWHAZ.
3. Screening of reference genes.
The copy number A of the SS18-SSX1 gene fusion in the positive sample of the SS18-SSX1 gene fusion variation is obtained through bioinformatics analysis, and meanwhile, the copy number B of reference genes HMBS, HPRT1, IPO8, ITGB7, JUN, LRP1, MRPL13 and MYC, TBP, YWHAZ is analyzed, and the results are shown in the following table.
TABLE 1 sample pathology assessment information and detection data
From the above results, the stability of the expression of the reference gene was evaluated. Firstly, eliminating reference genes with expression values which differ by more than 10 times: HMBS, HPRT1, ITGB7, MRPL13, TBP genes.
Then, the tumor content Z and the variable are evaluated by adopting linear regression analysis: fitting degree between the expression amounts of the fusion gene A and the reference gene B. Specific statistical analysis data are shown in the following table.
Table 2 tumor content Z and variables: r of expression level of fusion gene A and reference gene B 2
Performing line regression analysis on variables in statistics, and estimating parameters by using least square method, R 2 The ratio of the sum of squares of the regression to the sum of squares of the total dispersion, which represents the proportion of the sum of squares of the total dispersion that can be interpreted by the sum of squares of the regression, R 2 The closer to 1 between 0 and 1, the better the regression fit effect.
According to the results of the above table, the ratio B/A of the copy number of the reference gene YWHAZ to the copy number of the fusion gene SS18-SSX1 and the tumor cell content Z evaluated by pathologists have a high correlation, R 2 Closest to 1, 0.9693, a high fit is presented. The linear regression equation is shown in fig. 1, and is y= -0.1183x+0.9651.
Thus, the copy number of the reference gene YWHAZ and the copy number of the fusion gene SS18-SSX1 are obtained in synovial sarcoma fusion gene detection and substituted into the equation: z= -0.1183 XB/A+ 0.9651, the tumor burden of the sample can be calculated.
Example 2
A liquid biopsy (blood) from a synovial sarcoma fusion positive patient was taken and analyzed for tumor burden as follows.
1. And (5) data acquisition.
And obtaining the data obtained by the gene detection of the blood sample.
2. And (5) generating a letter and analyzing.
The fusion gene SS18-SSX1 copy number A in the sample is 21092 reads, and the internal reference gene YWHAZ copy number B is 34216 reads.
3. And (5) load analysis.
Substituting the tumor burden equation z= -0.1183 ×b/a+0.9651 obtained in example 1, the calculated tumor burden result was 0.77, i.e., 77%.
The pathological evaluation is carried out on the tissue sample corresponding to the sample, the evaluation result shows that the content of tumor tissue cells is 80%, the difference between the tumor tissue cells and the tumor tissue cells is within 5%, and the fact that the tumor content Z of the sample can be judged through the ratio (B/A) of the internal reference gene YWHAZ and the fusion gene SS18-SSX1 is indicated, so that reference is provided for accurate medical treatment.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. A system for analyzing the burden of a tumor caused by mutation of an RNA-level fusion gene, comprising:
and a data acquisition module: the method is used for acquiring off-machine data obtained by sample gene detection;
the letter generation analysis module: the method is used for respectively calculating the copy number of the target fusion gene and the copy number of the reference gene;
load analysis module: substituting the target fusion gene copy number A and the reference gene copy number B into the following tumor burden equation, and calculating to obtain a tumor burden Z of the sample;
Z＝k×B/A+a
wherein: k, a is a constant obtained by linear regression fitting;
the tumor burden equation is obtained by the following method:
taking a plurality of target fusion gene mutation positive samples as modeling samples, simultaneously obtaining pathological evaluation tumor load Z of a plurality of modeling samples, and obtaining target fusion gene copy number A and internal reference gene copy number B of the plurality of samples, wherein A and B are taken as independent variables, Z is taken as dependent variable, and linear regression analysis is carried out by a least square method to obtain a linear regression equation, namely the tumor load equation;
the reference gene meets the following inclusion criteria:
1) The reference gene is not a pseudogene;
2) The internal reference gene is moderately expressed, is obtained by sequencing transcriptomes of 16 organ tissues of normal people by researchers such as Thibaut according to a Human body map 2.0 transcriptome sequencing dataset, and is defined as moderately expressed by arranging FPKM geometric mean of the encoded protein genes in an ascending order and defining the genes between 33% and 67% positions;
3) The expression of the internal reference gene between tissues is stable, and the standard deviation [ log2 (RPKM) ] of the expression value between tissues is <1;
4) The reference gene expression level is independent of cell cycle and cell activation state;
and the reference gene does not have the following exclusion conditions:
1) The reference gene has different expression in normal and abnormal cells and tissues;
2) The reference gene exhibits cell cycle dependent expression;
3) The reference gene is located on the x chromosome.
2. The system for analyzing the burden on a tumor caused by mutation of an RNA level fusion gene according to claim 1, wherein the tumor is synovial sarcoma, the target fusion gene is SS18-SSX1 fusion gene, and the reference gene is selected from the group consisting of: IPO8, JUN, LRP1, MYC, YWHAZ.
3. The RNA level fusion gene mutation-causing tumor burden analysis system of claim 2, wherein the reference gene is selected from the group consisting of YWHAZ.
4. The system for analyzing tumor burden by RNA level fusion gene mutation according to claim 1, wherein the number of the modeling samples is equal to or greater than 6.
5. The system for analyzing tumor burden caused by RNA level fusion gene mutation according to claim 4, wherein the ratio of the sum of squares of regression to the sum of squares of total dispersion of the tumor burden equation is not less than 0.95.
6. The RNA level fusion gene mutation causing tumor burden analysis system of claim 1, wherein the pathology assessment tumor burden Z of the modeled sample is obtained by: taking a modeling sample, and evaluating the tumor content by a pathologist according to a conventional HE staining method.
7. The RNA level fusion gene mutation causing tumor burden analysis system of claim 1, wherein the tumor burden analysis system is used in liquid biopsies and blood tumors.
8. A method for analyzing tumor burden caused by mutation of an RNA level fusion gene for non-diagnostic therapeutic purposes, comprising the steps of:
and (3) data acquisition: acquiring off-machine data obtained by sample gene detection;
and (5) letter generation analysis: respectively calculating the copy number of the target fusion gene and the copy number of the reference gene;
load analysis: substituting the target fusion gene copy number A and the reference gene copy number B into the following tumor burden equation, and calculating to obtain a tumor burden Z of the sample;
Z＝k×B/A+a
wherein: k, a is a constant obtained by linear regression fitting;
the tumor burden equation is obtained by the following method:
taking a plurality of target fusion gene mutation positive samples as modeling samples, simultaneously obtaining pathological evaluation tumor load Z of a plurality of modeling samples, and obtaining target fusion gene copy number A and internal reference gene copy number B of the plurality of samples, wherein A and B are taken as independent variables, Z is taken as dependent variable, and linear regression analysis is carried out by a least square method to obtain a linear regression equation, namely the tumor load equation;
the reference gene meets the following inclusion criteria:
1) The reference gene is not a pseudogene;
2) The internal reference gene is moderately expressed, is obtained by sequencing transcriptomes of 16 organ tissues of normal people by researchers such as Thibaut according to a Human body map 2.0 transcriptome sequencing dataset, and is defined as moderately expressed by arranging FPKM geometric mean of the encoded protein genes in an ascending order and defining the genes between 33% and 67% positions;
3) The expression of the internal reference gene between tissues is stable, and the standard deviation [ log2 (RPKM) ] of the expression value between tissues is <1;
4) The reference gene expression level is independent of cell cycle and cell activation state;
and the reference gene does not have the following exclusion conditions:
1) The reference gene has different expression in normal and abnormal cells and tissues;
2) The reference gene exhibits cell cycle dependent expression;
3) The reference gene is located on the x chromosome.
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