CN109767811A - For predicting the construction method of the line style model of Tumor mutations load, predicting the method and device of Tumor mutations load - Google Patents
For predicting the construction method of the line style model of Tumor mutations load, predicting the method and device of Tumor mutations load Download PDFInfo
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Abstract
The construction method that the invention discloses a kind of for predicting the line style model of Tumor mutations load, the method and device for predicting Tumor mutations load.Wherein, which includes: S1, screens same sense mutation and the nonsynonymous mutation of protein encoding regions, calculates separately NsynAnd Nnon；S2 calculates Ltarget, calculate target area Tumor mutations load same sense mutation item: Tsys=Nsyn/Ltarget, calculate target area Tumor mutations load nonsynonymous mutation item: Tnon=Nnon/Ltarget；S3 establishes multivariate linear model, calculates the Tumor mutations load TMB:TMB=a of sample to be tested1Tsys+a2Tnon+a3log(Tnon).It applies the technical scheme of the present invention, can predict the Tumor mutations load of full exon, effectively reduce cost, shorten the period.
Description
Technical field
The present invention relates to field of biomedicine technology, in particular to a kind of for predicting the line of Tumor mutations load
The construction method of pattern type, the method and device for predicting Tumor mutations load.
Background technique
Tumor mutations load (TMB) refers to for the non-synonymous somatic mutation that the every megabasse of exons coding district occurs
Number is the completely new biomarker for predicting immunotherapeutic effects, has a good application prospect.Somatic mutation can change egg
Bai Xulie generates neoantigen.These neoantigens are identified as non-self antigen by self immune system, activate T cell, cause to be immunized
Reaction, therefore when Tumor mutations load is high, will generate more antigens, be conducive to immune system and kill tumour cell.Much grind
Studying carefully verified Tumor mutations load to immunotherapeutic effects is significant relevant.
Currently used Tumor mutations load testing method is the Lawrence team plan proposed on Nature in 2015
Slightly, Tumor mutations load condition is judged by calculating the somatic mutation number of full exon group (mean depth < 200X).So
And this method is full sequencing of extron group, at high cost, detection cycle is long.
Summary of the invention
The present invention is intended to provide a kind of construction method for predicting the line style model of Tumor mutations load, prediction tumour are prominent
The method and device of varying duty needs to carry out full exon group survey to solve Tumor mutations load testing method in the prior art
Sequence, at high cost, the long technical problem of detection cycle.
To achieve the goals above, according to an aspect of the invention, there is provided it is a kind of for predicting Tumor mutations load
Line style model construction method.The construction method obtains sample to be tested by sequencing and sequence analysis the following steps are included: S1
Accidental data, screen same sense mutation and the nonsynonymous mutation of protein encoding regions, calculate separately the same sense mutation of target area
Number NsynWith nonsynonymous mutation number Nnon；S2 calculates the protein-coding region length of field L of target areatarget, calculate targeting district
Domain Tumor mutations load same sense mutation item: Tsys=Nsyn/Ltarget, calculate target area Tumor mutations load nonsynonymous mutation item:
Tnon=Nnon/ Ltarget；S3 establishes multivariate linear model, calculates the Tumor mutations load TMB:TMB=a of sample to be tested1Tsys+
a2Tnon+ a3log(Tnon), wherein a1、a2And a3It is fitted to obtain by existing database data.
Further, existing database includes TCGA database.
According to another aspect of the present invention, a kind of method for predicting Tumor mutations load is provided.This method includes following
Step: S1 obtains the tumor sample of same patient respectively and normal sample and extracts DNA；S2 is captured former according to target area
Reason captures tumor-related gene using probe；S3 carries out sequencing by high-throughput method and sequence is analyzed, obtains tumor sample
Accidental data；And S4, according to as claimed in claim 1 for predicting the construction method of the line style model of Tumor mutations load
The line style model prediction Tumor mutations load TMB of building.
Further, S3 further include: select the sequencing sequence of high quality, removal N content is greater than 5% sequence, and removal contains
There is the sequence of connector.
Further, the sequence analysis in S3 includes: and uses to compare software for tumor sample DNA and normal sample DNA ratio
To genome is referred to, then using variation inspection software, detection obtains the accidental data of tumor sample.
Further, S4 further include: mutational site is annotated using ANNOVAR software, obtains the same of target area
Justice and nonsynonymous mutation, and calculate separately its number.
In accordance with a further aspect of the present invention, a kind of device for predicting Tumor mutations load is provided.The device includes: device
For storing the module perhaps run or module as the component part of device；Wherein, module is software module, software module
The method for being used to execute above-mentioned prediction Tumor mutations load for one or more, software module.
It applies the technical scheme of the present invention, passes through the Tumor mutations of the full exon of Tumor mutations load prediction of target area
Load effectively reduces cost, shortens the period.
Detailed description of the invention
The accompanying drawings constituting a part of this application is used to provide further understanding of the present invention, and of the invention shows
Examples and descriptions thereof are used to explain the present invention for meaning property, does not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 shows the dependency graph that method in embodiment 1 calculates gained TMB and full exon data calculating gained TMB；
And
Fig. 2 shows TMB (WXS) intuitively comparings obtained by TMB (549panel) obtained by method in embodiment 1 and full exon
Figure；
Fig. 3 shows the thumbnail being mutated obtained by PM00G18**0228 sample in embodiment 2.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.Below in conjunction with embodiment, the present invention will be described in detail.
A kind of typical embodiment is invented at all, is provided a kind of for predicting the structure of the line style model of Tumor mutations load
Construction method.Method includes the following steps: S1, the accidental data of sample to be tested is obtained by sequencing and sequence analysis, screens egg
The same sense mutation of white coding region and nonsynonymous mutation calculate separately the same sense mutation number N of target areasynIt dashes forward with non-synonymous
Become number Nnon；S2 calculates the protein-coding region length of field L of target areatarget；It is same to calculate target area Tumor mutations load
Justice mutation item: Tsys=Nsyn /Ltarget；Calculate target area Tumor mutations load nonsynonymous mutation item: Tnon=Nnon/Ltarget；
S3 establishes multivariate linear model, calculates the Tumor mutations load TMB:TMB=a of sample to be tested1Tsys+a2Tnon+a3log(Tnon),
Wherein, a1、a2And a3It is fitted to obtain by existing database data.
It applies the technical scheme of the present invention, is sequenced by targeting and calculates the effectively replacement full exon sequencing of Tumor mutations load
Tumor mutations load is calculated, cost is effectively reduced, shortens the period.
A kind of typical embodiment according to the present invention, existing database include TCGA database.For example, in the present invention
In one embodiment, it is fitted by 10092 WES data of the 33 kinds of tumours downloaded from TCGA.Because generally acknowledging WES at present
The TMB of calculating is most quasi-, so being subject to TMB obtained by WES when fitting, PANEL gene in the present embodiment is extracted from WES data
Abrupt information, be fitted therewith, finally obtain model parameter.
A kind of typical embodiment is invented at all, and a kind of method for predicting Tumor mutations load is provided.This method include with
Lower step: S1 obtains the tumor sample of same patient respectively and normal sample and extracts DNA；S2 is captured according to target area
Principle captures tumor-related gene using probe；S3 carries out sequencing by high-throughput method and sequence is analyzed, and obtains sequencing letter
Cease the accidental data of tumor sample；And S4, according to above-mentioned for predicting the construction method of the line style model of Tumor mutations load
The line style model prediction Tumor mutations load TMB of building.
Calculating Tumor mutations load, which is sequenced, by targeting effectively replaces the sequencing of full exon to calculate Tumor mutations load, effectively
Reduce cost, shorten the period.
Preferably, S3 further include: select the sequencing sequence of high quality, removal N content is greater than 5% sequence, and removal contains
The sequence of connector, to further increase the accuracy of prediction.Sequence point in a typical embodiment of the invention, in S3
Analysis includes: that tumor sample DNA and normal sample DNA are compared reference genome using comparison software, then using variation inspection
Software is surveyed, detection obtains the accidental data of the tumor sample.
Preferably, S4 further include: mutational site is annotated using ANNOVAR software, obtains the synonymous of target area
And nonsynonymous mutation, and calculate separately its number.
A kind of typical embodiment is invented at all, and a kind of device for predicting Tumor mutations load is provided.The device includes:
Device is used to store the module perhaps run or module is the component part of device；Wherein, module is software module, software
Module is one or more, and software module is for executing any of the above-described kind of method.
Beneficial effects of the present invention are further illustrated below in conjunction with embodiment.
Embodiment 1
From TCGA database download 33 kinds of tumours (33 kinds of tumours be respectively ACC, KIRC, PRAD, BLCA, KIRP, READ,
BRCA、LAML、SARC、CESC、LGG、SKCM、CHOL、LIHC、STAD、COAD、LUAD、 TGCT、DLBC、LUSC、THCA、
ESCA, MESO, THYM, GBM, OV, UCEC, HNSC, PAAD, UCS, KICH, PCPG and UVM) 10092 WES data, from
The middle targeted capture region mutagenesis information for extracting customized 549 genes calculates target area by following multivariate linear model
TMB。
S1 obtains the accidental data of sample to be tested by sequencing and sequence analysis, screens the synonymous prominent of protein encoding regions
Change and nonsynonymous mutation, calculate separately the same sense mutation number N of target areasynWith nonsynonymous mutation number Nnon；
S2 calculates the protein-coding region length of field L of target areatarget；It is synonymous prominent to calculate target area Tumor mutations load
Variable: Tsys=Nsyn/Ltarget；Calculate target area Tumor mutations load nonsynonymous mutation item: Tnon=Nnon/Ltarget；
S3 establishes multivariate linear model, calculates the Tumor mutations load TMB:TMB=a of sample to be tested1Tsys+a2Tnon+
a3log(Tnon)。
The TMB that WES is directly calculated is compared as standard, Fig. 1 shows the present embodiment method and calculates gained TMB
The dependency graph of gained TMB is calculated with full exon data.Abscissa is that the present embodiment method calculates gained TMB, and ordinate is
A exon calculates gained TMB, correlation R2=0.9898.Illustrate the accuracy of the modeling method of the invention.
Fig. 2 shows TMB obtained by TMB (549panel) obtained by the present embodiment method in 33 kinds of tumours and full exon
(WXS) intuitively comparing figure.Abscissa is different tumours, and ordinate calculates gained TMB.
Embodiment 2
The tumor sample and normal control sample of sample PM00G18**0228 are obtained, and extracts DNA respectively；By free
549 gene panel simultaneously to tumor sample and normal sample DNA carry out capture build library, sequencing；Then it compares, search and dash forward
Become, result annotation, mutation filtering, finally obtains the detailed abrupt information (16466, see Fig. 3) of subject.
Abrupt information classifies to obtain 5027 nonsynonymous mutation (Nnon) and 11439 same sense mutation (Nsyn), target area
Protein-coding region length of field be 1.373692Mbp (Ltarget), target area Tumor mutations load nonsynonymous mutation is calculated
Item (Tnon) and same sense mutation item (Tsys), then substitute into established multivariate linear model be calculated sample to be tested tumour it is prominent
Varying duty is 82.9.
Fig. 3 shows the thumbnail being mutated obtained by PM00G18**0228 sample.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of for predicting the construction method of the line style model of Tumor mutations load, which comprises the following steps:
S1, by sequencing and sequence analysis obtain sample to be tested accidental data, screen protein encoding regions same sense mutation and
Nonsynonymous mutation calculates separately the same sense mutation number N of target areasynWith nonsynonymous mutation number Nnon；
S2 calculates the protein-coding region length of field L of target areatarget, calculate the load same sense mutation of target area Tumor mutations
: Tsys=Nsyn/Ltarget, calculate target area Tumor mutations load nonsynonymous mutation item: Tnon=Nnon/Ltarget；
S3 establishes multivariate linear model, calculates the Tumor mutations load TMB of sample to be tested:
TMB=a1Tsys+a2Tnon+a3log(Tnon), wherein a1、a2And a3It is fitted to obtain by existing database data.
2. construction method according to claim 1, which is characterized in that the existing database includes TCGA database.
3. a kind of method for predicting Tumor mutations load, which comprises the following steps:
S1 obtains the tumor sample of same patient respectively and normal sample and extracts DNA；
S2 captures principle according to target area and captures tumor-related gene using probe；
S3 carries out sequencing by high-throughput method and sequence is analyzed, obtains the accidental data of tumor sample；And
S4, according to the construction method building of the line style model as described in claim 1 for predicting Tumor mutations load
Line style model prediction Tumor mutations load TMB.
4. according to the method described in claim 3, it is characterized in that, the S3 further include: the sequencing sequence for selecting high quality is gone
Except N content is greater than 5% sequence, the sequence containing connector is removed.
5. according to the method described in claim 4, it is characterized in that, the sequence analysis in the S3 includes: using comparison software
Tumor sample DNA and normal sample DNA are compared into reference genome, then using variation inspection software, detection obtains described
The accidental data of tumor sample.
6. according to the method described in claim 4, it is characterized in that, the S4 further include: using ANNOVAR software to mutation position
Point is annotated, and obtains the synonymous and nonsynonymous mutation of target area, and calculate separately its number.
7. a kind of device for predicting Tumor mutations load characterized by comprising
Described device is used to store the module perhaps run or the module is the component part of described device；Wherein, described
Module is software module, and the software module is one or more, and the software module is for executing the claims 3 to 6
Any one of described in method.
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