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Neoantigen identification using hotspots

A technology of antigens and alleles, applied in the determination/testing of microorganisms, medical raw materials derived from mammals, instruments, etc., can solve the problems of missing candidate neoantigens, inefficient use of autoimmunity in vaccines, etc., and achieve the goal of speeding up the process Effect

Pending Publication Date: 2020-07-28
GRITSTONE BIO INC
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Problems solved by technology

[0007] Finally, standard approaches to tumor genome and transcriptome profiling may miss somatic mutations that generate candidate neoantigens due to suboptimal conditions for library construction, exome and transcriptome capture, sequencing, or data analysis
Likewise, standard tumor profiling methods may inadvertently contribute to sequence artifacts or germline polymorphisms as neoantigens, leading to inefficient use of vaccine potency or risk of autoimmunity, respectively

Method used

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example

[0417] In another embodiment, the deviation parameter θ h 0 May be shared by gene families of the MHC allele h. That is, the bias parameter θ of the MHC allele h h 0 can be equal to θ 基因(h) 0 , where gene (h) is the gene family of the MHC allele h. For example, the MHC class I alleles HLA-A*02:01, HLA-A*02:02, and HLA-A*02:03 can be assigned to the "HLA-A" gene family, and these MHC alleles The respective bias parameters θ h 0 Can be shared. As another example, the MHC class II alleles HLA-DRB1:10:01, HLA-DRB1:11:01, and HLA-DRB3:01:01 may be assigned to the "HLA-DRB" gene family, and these MHC alleles Gene's respective bias parameter θ h 0 Can be shared.

[0418] Going back to equation (2), as an example, when using the affine correlation function g h (·) Of the m = 4 different MHC alleles identified, peptide p k The probability of being presented by the MHC allele h=3 can be given by:

[0419]

[0420] where x 3 k is the allelic interaction variable for t...

Embodiment 1

[0467] VIII.C.1. Example 1: Maximum of Independent Allele Models

[0468] In one embodiment, the training module 316 causes peptide p associated with a set of multiple MHC alleles H k The estimated probability of presentation u k With the probability of presentation u of each MHC allele h in set H determined based on cells expressing the monoallele k h∈H The variation of is modeled as described above in connection with equations (2)-(11). Specifically, the presentation probability u k can be u k h∈H any function of . In one embodiment, as shown in equation (12), this function is a maximum function and renders the likelihood u k can be determined as the maximum probability of presentation for each MHC allele h in set H.

[0469]

[0470] VIII.C.2. Example 2.1: The Funciton-of-Sums Model

[0471] In one embodiment, the training module 316 makes peptide p by k The estimated probability of presentation u k Modeling:

[0472]

[0473] where element a h k For...

Embodiment 22

[0483] VIII.C.3. Example 2.2: Functional Model Using the Sum of Allelic Non-Interacting Variables

[0484] In one embodiment, the training module 316 incorporates allelic non-interaction variables and makes peptide p by k The estimated probability of presentation u k Modeling:

[0485]

[0486] where w k Indicates the encoded related peptide p k The allelic non-interaction variable for . Specifically, the parameter set θ for each MHC allele h h and the set of parameters about the allelic non-interacting variables θ w The value of θ can be obtained by making about θ h and θ w is determined by minimizing a loss function of , where i is each instance in the subset S of training data 170 produced by cells expressing a single MHC allele and / or by cells expressing multiple MHC alleles. Correlation function g w The correlation function g introduced in Section VIII.B.3 above can be expressed as w any of the forms.

[0487] Therefore, according to equation (14), the fun...

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Abstract

A method for identifying neoantigens that are likely to be presented on a surface of tumor cells of a subject. Peptide sequences of tumor neoantigens are obtained by sequencing the tumor cells of thesubject. The peptide sequence of each of the neoantigens is associated with one or more k-mer blocks of a plurality of k-mer blocks of the nucleotide sequencing data of the subject; The peptide sequences and the associated k-mer blocks are input into a machine- learned presentation model to generate presentation likelihoods for the tumor neoantigens, each presentation likelihood representing the likelihood that a neoantigen is presented by an MHC allele on the surfaces of the tumor cells of the subject. A subset of the neoantigens is selected based on the presentation likelihoods.

Description

Background technique [0001] Therapeutic vaccines and T cell therapies based on tumor-specific neoantigens hold great promise as the next generation of personalized cancer immunotherapy. 1–3 Cancers with a high mutational burden, such as non-small cell lung cancer (NSCLC) and melanoma, represent particularly interesting targets for such therapies, given the relatively high likelihood of neoantigen production. 4,5 Early evidence suggests that neoantigen-based vaccinations can elicit T-cell responses 6 And T-cell therapy targeting neoantigens was able to induce tumor regression in selected patients in some cases. 7 Both MHC class I and MHC class II have an impact on T cell responses 70-71 . [0002] However, the identification of neoantigens and the T cells that recognize them have become an important part of assessing tumor response. 77,110 , check tumor progression 111 and design the next generation of personalized therapies 112 main challenge. Current neoantigen identif...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B40/00G16B40/10A61K35/13C12Q1/68G01N33/50G01N33/574
CPCC12Q1/6886G01N33/50G01N33/56977G01N33/574G01N33/6878C12Q2600/156C12Q2600/158G16B50/30G16B40/00G16B30/00G16B5/20G16B40/20G06N3/04
Inventor B·布里克-沙利文T·F·鲍彻R·耶冷斯凯
Owner GRITSTONE BIO INC
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