CN107704727A - Neoantigen Activity Prediction and sort method based on tumour neoantigen characteristic value - Google Patents
Neoantigen Activity Prediction and sort method based on tumour neoantigen characteristic value Download PDFInfo
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Abstract
The invention discloses a kind of neoantigen immunocompetence marking based on tumour neoantigen characteristic value and sort method, comprise the following steps：The WGS/WES, RNA seq sequencing datas of tumour normal sample input, the prediction of tumour somatic mutation and annotation, associated eigenvalue calculate；MHC I combinations neoantigen prediction based on tumour somatic mutation, associated eigenvalue calculate；The extraction of neoantigen associated eigenvalue；The setting of neoantigen activity scoring functions；Neoantigen sequence based on neoantigen activity scoring functions.Present invention analysis first calculates the mutation of tumour body cell and completes mutation annotation, calculates partial feature value, then predicts MHC I combination neoantigens, calculate partial feature value；All associated eigenvalues of tumour neoantigen are extracted, and then set neoantigen activity scoring functions, neoantigen is ranked up finally by neoantigen activity scoring functions.This method is highly efficient accurate compared with traditional screening technique, has important application value for tumour immunotherapy.
Description
Technical field
The present invention relates to immunotherapy of tumors field, in particular it relates to a kind of new anti-based on tumour neoantigen characteristic value
Former activity marking and sort method.
Background technology
In recent years, immunotherapy of tumors yields unusually brilliant results, and clinical test constantly obtains spy and broken, and cure rate and effective remission rate are held
Continuous lifting.The efficient precisely screening of tumour neoantigen is work of crucial importance and basic in tumour immunotherapy, particularly
It is particularly important to tumour immunotherapies such as TCR-T/TIL, individuation vaccines.
At present, the scheme passed through at present for the screening technique of tumour neoantigen is two steps：Step 1: based on swollen
The WGS/WES data of knurl-normal structure, the instruments such as Mutect/Varscan are called to calculate the gene mutation of tumour cell；Step
2nd, NetMHCpan scheduling algorithms prediction MHC-I combination neoantigens are called.
There is presently no activity of the effective method based on antigen to be ranked up, to lift antigen selection efficiency.Above-mentioned side
Due to not carrying out active sequence to the MHC-I combinations neoantigen that prediction obtains in case, therefore can be huge to bringing for experimental verification
Big workload, cause tumour neoantigen screening efficiency it is low.
The content of the invention
The present invention is directed to above-mentioned the deficiencies in the prior art, there is provided a kind of based on the new of tumour neoantigen characteristic value
Antigen active is given a mark and sort method, can substantially reduce the workload of experimental verification, and further realize the height of tumour neoantigen
Effect precisely screening.
The technical proposal of the invention is realized in this way：
Based on tumour neoantigen characteristic value neoantigen immunocompetence prediction and sort method, it is characterised in that including with
Lower step：
(1), the input of WGS/WES, RNA-SEQ sequencing data of tumour-normal sample：Input tumour-normal sample
Full genome sequencing data WGS or full sequencing of extron group data WES, transcript profile sequencing data RNA-SEQ；
(2), the prediction of tumour somatic mutation and annotation, the calculating of associated eigenvalue：Sequencing based on step (1) input
Data, call Varscan or Mutect tool analysis to calculate tumour somatic mutation, call VEP (Variant Effect
Prediction) instrument complete mutation annotation, call PyClone, Kallisto, Varscan or Mutect instrument calculate as
Lower eigenvalue：Mutator clone ratio, mutator expression value TPM, allelic mutation frequency VAF；
(3) prediction of MHC-I combinations neoantigen, the calculating of associated eigenvalue based on tumour somatic mutation：Based on step
(2) tumour somatic mutation and annotation data in, NetMHCpan, Netchop, OptiType instrument prediction MHC-I knots are called
Neoantigen is closed, and is calculated such as lower eigenvalue：It is mutated peptide fragment and MHC affinity sequence percentage, unmutated peptide fragment and MHC is affine
Power sequence percentage, peptide fragment shearing present efficiency；
(4) extraction of all associated eigenvalues of neoantigen：For the MHC-I combination neoantigens of prediction in step (3), extraction
Go out all associated eigenvalues of tumour neoantigen；
(5) setting of neoantigen activity scoring functions：For the neoantigen characteristic value of extraction in step (4), set new anti-
Former active scoring functions；
(6) the neoantigen sequence based on neoantigen activity scoring functions：By neoantigen activity scoring functions to neoantigen
It is ranked up.
Preferably, in step (4), neoantigen associated eigenvalue includes Rm、A、Rn, E, NC, CL, wherein：
Rm- mutation peptide fragment and MHC affinity sequence percentage, are calculated by NetMHCpan；
A-allelic mutation frequency VAF, is calculated by Varscan/Mutect/Strelka2；
Rn- unmutated peptide fragment and MHC affinity sequence percentage, are calculated by NetMHCpan；
E-mutator expression value TPM, is calculated by Kallisto；
NC-peptide fragment shearing presents efficiency, is calculated by netchop；
CL-mutator clone's ratio, is calculated by pyclone.
Preferably, in step (5), the neoantigen Activity Prediction scoring functions of proposition are：
Neo_Score=abundancedissimilarityclonality；
Clonality=NCCL；
Abundance=L (Rm) Atanh (E/k)；
Dissimilarity=(1-L (Rn)/2))；
Wherein：L (x)=1/ (1+e5(x-2)), tanh (x) is hyperbolic tangent function；
K is transcript gene expression abundance threshold value, default value 1.
Preferably, in step (6), algorithmic procedure is ranked up such as by neoantigen Activity Prediction function pair neoantigen
Under：
A), for the MHC-I combination neoantigens of all predictions, neoantigen Activity Prediction function Neo_score is called to calculate
Go out the predicted value of neoantigen activity；
B), the predicted value based on neoantigen activity, is ranked up using quick sorting algorithm to neoantigen；
C), neoantigen ranking results are exported.
The design philosophy of the invention and beneficial effect for employing above-mentioned technical proposal be：
Technical scheme, it is proposed that a kind of tumour neoantigen Activity Prediction scoring functions, based on neoantigen activity
Anticipation function carries out active sequence to the MHC-I combinations neoantigen predicted, so as to realize efficiently, accurately tumour neoantigen sieves
Choosing.
This function is based on the generation of tumour neoantigen, cutting is transported, neoantigen is combined the design of this complete procedure with MHC, beats
Function is divided to be divided into 3 parts, wherein, it is to the efficiency NC of small peptide and newly anti-by being mutated that Clonal (clonality) weighs neoantigen
The ratio CL that original is distributed in all tumour cells, it is an important factor for influenceing tumor vaccine curative effect；Abundance (abundance) weighs
Amount neoantigen expression quantity and neoantigen are combined with MHC-I and form the efficiency of pMHC compounds, neoantigen mutator expression quantity E
Higher, allelic mutation frequency A is higher, binding affinity R between compoundmHigher (IC50 values are smaller), immunogenicity is then got over
By force；Dissimilar degree (dissimilarity) weighs difference R of the mutation peptide fragment with corresponding normal peptide fragment affinityn, because maincenter is resistance to
Existed by mechanism, the two difference is bigger, and the specificity of neoantigen is stronger, and the side effect for treatment is also smaller.Two in function
Individual mapping function is used to normalize (0 to 1) calculated value, and threshold value 2 is that peptide fragment-MHC binding affinities screen threshold value, tanh in L (x)
(x/k) when ensureing that neoantigen gene expression abundance exceedes given threshold k, function value changes tend towards stability.
This Activity Prediction function considers the combined influence factor during neoantigen produces, and the antigen after sequence is more intentional
Justice and application value.
Brief description of the drawings
Fig. 1 is the neoantigen activity methods and sort method based on tumour neoantigen characteristic value described in the embodiment of the present invention
Schematic diagram.
Embodiment
The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that described herein preferred real
Apply example to be merely to illustrate and explain the present invention, be not intended to limit the present invention.
Embodiment one：
As shown in figure 1, a kind of neoantigen activity methods and sort method based on tumour neoantigen characteristic value, including it is following
Step：
Step 101：The input of WGS/WES, RNA-seq sequencing data of tumour-normal sample (uses melanoma patient
Sample one mel_21, Science 2015:Carreno B M,Magrini V,Beckerhapak M,et al.Cancer
immunotherapy.A dendritic cell vaccine increases the breadth and diversity of
melanoma neoantigen-specific T cells.[J].Science,2015,348(6236):803-8.)
Step 102：The prediction of tumour somatic mutation and annotation, the calculating of associated eigenvalue：Based on tumour-normal sample
WGS/WES, RNA-seq sequencing data, call the tool analysis such as Varscan/Mutect to calculate tumour somatic mutation, adjust
Mutation is completed with VEP (Variant Effect Prediction) instrument to annotate, calling PyClone, Kallisto,
Varscan/Mutect instruments are calculated such as lower eigenvalue：Mutator is cloned than CL, mutator expression value TPM, equipotential base
Because of frequency of mutation VAF.By taking characteristic value corresponding to active peptide fragment in document as an example, the mel_21 of patient's sample one 3 peptide fragments pair
The E (TPM) and A (VAF) value that should be calculated are as follows：
Table 1
Step 103：The prediction of MHC-I combinations neoantigen, the calculating of associated eigenvalue based on tumour somatic mutation：It is based on
Tumour somatic mutation and annotation data in step (1), call the prediction of NetMHCpan, Netchop, OptiType instrument
MHC-I combination neoantigens, and calculate such as lower eigenvalue：It is mutated peptide fragment and MHC affinity sequence percentage Rm, unmutated peptide fragment
With MHC affinity sequence percentage Rn, peptide fragment shearing present efficiency NC.Using in document characteristic value corresponding to active peptide fragment as
Example, the R that the mel_21 of patient's sample one 3 peptide fragments correspondingly calculatem,RnIt is as follows with NC values：
Table 2
Step 104：The extraction of neoantigen associated eigenvalue：For the MHC-I combination neoantigens predicted in step 103, carry
All associated eigenvalues of tumour neoantigen are taken out, the mel_21 of patient's sample one neoantigen and characteristic value are shown in Table 1, table 2；
Step 105：The setting of neoantigen activity scoring functions：For the neoantigen characteristic value extracted in step 104, setting
Neoantigen activity scoring functions；
Step 106：Neoantigen sequence based on neoantigen activity scoring functions：By neoantigen activity scoring functions to new
Antigen is ranked up, and table 3 gives the marking (Neo_ of the neoantigen through the checking of PMHC activity experiments in the mel_21 of patient's sample one
Score the sequence (Rank)) and in complete or collected works.
Table 3
In the neoantigen that 3 identify, CLNEYHLFL can to stimulate DC cells just to show before using tumor vaccine
The immunocompetence of CD8+T cells is activated, remaining 2 are then after using vaccine enhancing immune system ability, are possessed in various degree
Immunocompetence.We have seen that KMIGNHLWV and CLNEYHLFL is in sequence Top 3 (total candidate's neoantigen number 94) position.
And AMFWSVPTS is either tested in still human tumor microenvironment in vitro, because expression quantity is relatively low, its immunogenicity is determined
It is on the weak side with immune response, be in 33 in our ranking results, experimental result and our prediction sequence quite it is identical.
Embodiment two：
As shown in figure 1, a kind of neoantigen activity methods and sort method based on tumour neoantigen characteristic value, including it is following
Step：
Step 101：The input of WGS/WES, RNA-seq sequencing data of tumour-normal sample (uses melanoma patient
Sample two mel_38, Science 2015:Carreno B M,Magrini V,Beckerhapak M,et al.Cancer
immunotherapy.A dendritic cell vaccine increases the breadth and diversity of
melanoma neoantigen-specific T cells.[J].Science,2015,348(6236):803-8.)
Step 102：The prediction of tumour somatic mutation and annotation, the calculating of associated eigenvalue：Based on tumour-normal sample
WGS/WES, RNA-seq sequencing data, call the tool analysis such as Varscan/Mutect to calculate tumour somatic mutation, adjust
Mutation is completed with VEP (Variant Effect Prediction) instrument to annotate, calling PyClone, Kallisto,
Varscan/Mutect instruments are calculated such as lower eigenvalue：Mutator is cloned than CL, mutator expression value TPM, equipotential base
Because of frequency of mutation VAF；By taking characteristic value corresponding to active peptide fragment in document as an example, the mel_38 of patient's sample two 3 peptide fragments pair
The E (TPM) and A (VAF) value that should be calculated are as follows：
Table 4
Step 103：The prediction of MHC-I combinations neoantigen, the calculating of associated eigenvalue based on tumour somatic mutation：It is based on
Tumour somatic mutation and annotation data in step (1), call the prediction of NetMHCpan, Netchop, OptiType instrument
MHC-I combination neoantigens, and calculate such as lower eigenvalue：It is mutated peptide fragment and MHC affinity sequence percentage Rm, unmutated peptide fragment
With MHC affinity sequence percentage Rn, peptide fragment shearing present efficiency NC；Using in document characteristic value corresponding to active peptide fragment as
Example, the R that the mel_38 of patient's sample two 3 peptide fragments correspondingly calculatem,RnIt is as follows with NC values：
Table 5
Step 104：The extraction of neoantigen associated eigenvalue：For the MHC-I combination neoantigens of prediction in step (2), carry
All associated eigenvalues of tumour neoantigen are taken out, the mel_38 of patient's sample two neoantigen and characteristic value are shown in Table 4, table 5；
Step 105：The setting of neoantigen activity scoring functions：For the neoantigen characteristic value of extraction in step (3), setting
Neoantigen activity scoring functions；
Step 106：Neoantigen sequence based on neoantigen activity scoring functions：By neoantigen activity scoring functions to new
Antigen is ranked up, and table 6 gives the marking (Neo_ of the neoantigen through the checking of PMHC activity experiments in the mel_38 of patient's sample two
Score the sequence (Rank)) and in complete or collected works
Table 6
In the neoantigen that 3 identify, FLYNLLTRVY is to stimulate just to show before DC cells using tumor vaccine
The immunocompetence of CD8+T cells can be activated, remaining 2 are then after using vaccine enhancing immune system ability, possess different journeys
The immunocompetence of degree.We have seen that QLSCISTYV and FLYNLLTRVY is in Top 20 (total candidate's neoantigen number 117).And
KLMNIQQKL either in vitro experiment or human tumor microenvironment in, because expression quantity is relatively low, determine its immunogenicity and
Immune response is on the weak side, is in 66 in our ranking results, experimental result and our prediction sequence quite it is identical.
In summary, it is proposed that neoantigen immunocompetence scoring functions, can be exempted from effectively measurement neoantigen
Epidemic disease activity, help is provided with immunization therapy for clinical trial and tumor research.
Claims (4)
1. the prediction of neoantigen immunocompetence and sort method based on tumour neoantigen characteristic value, it is characterised in that including following
Step：
(1), the input of WGS/WES, RNA-SEQ sequencing data of tumour-normal sample：Input the full base of tumour-normal sample
Because of sequencing data WGS or full sequencing of extron group data WES, transcript profile sequencing data RNA-SEQ；
(2), the prediction of tumour somatic mutation and annotation, the calculating of associated eigenvalue：Sequencing number based on step (1) input
According to calling Varscan or Mutect tool analysis calculates tumour somatic mutation, calls VEP (Variant Effect
Prediction) instrument complete mutation annotation, call PyClone, Kallisto, Varscan or Mutect instrument calculate as
Lower eigenvalue：Mutator clone ratio, mutator expression value TPM, allelic mutation frequency VAF；
(3) prediction of MHC-I combinations neoantigen, the calculating of associated eigenvalue based on tumour somatic mutation：Based in step (2)
Tumour somatic mutation and annotation data, call NetMHCpan, Netchop, OptiType instrument prediction MHC-I to combine new
Antigen, and calculate such as lower eigenvalue：Peptide fragment is mutated to arrange with MHC affinity sequence percentage, unmutated peptide fragment and MHC affinity
Sequence percentage, peptide fragment shearing present efficiency；
(4) extraction of all associated eigenvalues of neoantigen：For the MHC-I combination neoantigens of prediction in step (3), extract swollen
All associated eigenvalues of knurl neoantigen；
(5) setting of neoantigen activity scoring functions：For the neoantigen characteristic value of extraction in step (4), setting neoantigen is lived
Property scoring functions；
(6) the neoantigen sequence based on neoantigen activity scoring functions：Neoantigen is carried out by neoantigen activity scoring functions
Sequence.
2. the method according to claim 11, it is characterized in that：
In step (4), neoantigen associated eigenvalue includes Rm、A、Rn, E, NC, CL, wherein：
Rm- mutation peptide fragment and MHC affinity sequence percentage, are calculated by NetMHCpan；
A-allelic mutation frequency VAF, is calculated by Varscan/Mutect/Strelka2；
Rn- unmutated peptide fragment and MHC affinity sequence percentage, are calculated by NetMHCpan；
E-mutator expression value TPM, is calculated by Kallisto；
NC-peptide fragment shearing presents efficiency, is calculated by netchop；
CL-mutator clone's ratio, is calculated by pyclone.
3. the method according to claim 11, it is characterized in that：
In step (5), the neoantigen Activity Prediction scoring functions of proposition are：
Neo_Score=abundancedissimilarityclonality；
Clonality=NCCL；
Abundance=L (Rm) Atanh (E/k)；
Dissimilarity=(1-L (Rn)/2))；
Wherein：L (x)=1/ (1+e5(x-2)), tanh (x) is hyperbolic tangent function；
K is transcript gene expression abundance threshold value, default value 1.
4. the method according to claim 11, it is characterized in that：In step (6), newly resisted by neoantigen Activity Prediction function pair
It is as follows that original is ranked up algorithmic procedure：
A), for the MHC-I combination neoantigens of all predictions, neoantigen Activity Prediction function Neo_score is called to calculate newly
The predicted value of antigen active；
B), the predicted value based on neoantigen activity, is ranked up using quick sorting algorithm to neoantigen；
C), neoantigen ranking results are exported.
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| CN110706742A (en) * | 2019-09-30 | 2020-01-17 | 中生康元生物科技(北京)有限公司 | Pan-cancer tumor neoantigen high-throughput prediction method and application thereof |
| CN111415707A (en) * | 2020-03-10 | 2020-07-14 | 四川大学 | Prediction method of clinical individualized tumor neoantigen |
| CN113762416A (en) * | 2021-10-15 | 2021-12-07 | 南京澄实生物科技有限公司 | Antigen immunogenicity prediction method and system based on multi-mode depth coding |
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