Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Alphavirus neoantigen vectors

A technology of alpha virus and antigen, applied in the field of alpha virus neoantigen carrier, which can solve the problems of inefficient use of autoimmunity and omission of candidate neoantigens in vaccines

Pending Publication Date: 2019-12-24
GRITSTONE BIO INC
View PDF25 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] 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
[0010] In addition to the challenges of current neoantigen prediction methods, there are certain challenges with the available vector systems for neoantigen delivery in humans, many of which are derived from human

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Alphavirus neoantigen vectors
  • Alphavirus neoantigen vectors
  • Alphavirus neoantigen vectors

Examples

Experimental program
Comparison scheme
Effect test

example

[0707] In another embodiment, the deviation parameter θ h 0 Can be shared by the gene family 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 class I MHC 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 bias parameter θ for each of h 0 Can be shared. As another example, the class II MHC alleles HLA-DRB1:10:01, HLA-DRB1:11:01, and HLA-DRB3:01:01 can be assigned to the "HLA-DRB" gene family, and these MHC alleles Bias parameter θ for each of the genes h 0 Can be shared.

[0708] Returning to equation (2), as an example, using the affine dependency function g h (·) Among the identified m = 4 different MHC alleles, peptide p k The probability of being presented by the MHC allele h=3 can be generated by:

[0709]

[0710] where x 3 k is the allelic interaction variable...

Embodiment 1

[0750] X.C.1. Example 1: Maximum value of independent allele model

[0751] In one embodiment, the training module 316 causes the peptide p associated with a set of multiple 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 likelihood 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 the probability u k can be determined as the maximum probability of presentation for each MHC allele h in set H.

[0752]

Embodiment 21

[0753] X.C.2. Example 2.1: Sum function model

[0754] In one embodiment, the training module 316 makes peptide p by k The estimated probability of presentation u k Modeling:

[0755]

[0756] where element a h k For the peptide sequence p k Associated multiple MHC alleles H is 1 and x h k Indicates the encoded related peptide p k and the allelic interaction variable for the corresponding MHC allele. The set of parameters θ for each MHC allele h h The value of θ can be obtained by making about θ h is determined by minimizing the loss function for 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. dependency function g h The dependency function g introduced in Section X.B.1 above can be h any of the forms.

[0757] According to equation (13), the peptide sequence p k The probability of presentation to be presented by one or more MHC alleles h can be calculated by a...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

Disclosed herein are alphavirus vectors that include neoantigen-encoding nucleic acid sequences derived from a tumor of a subject. Also disclosed are nucleotides, cells, and methods associated with the vectors including their use as vaccines.

Description

[0001] sequence listing [0002] This application contains a Sequence Listing, which has been filed by EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy created on XX, XX, 20XX, named XXXXXUS_sequencelisting.txt, and is X,XXX,XXX bytes in size. Background technique [0003] Therapeutic vaccines based on tumor-specific neoantigens hold great promise as a new generation of personalized cancer immunotherapy. 1–3 Given the relatively high probability of generating neoantigens, cancers with high mutational burdens, such as non-small cell lung cancer (NSCLC) and melanoma, are particularly attractive targets for such therapies. 4,5 Early evidence shows that neoantigen-based vaccination elicits T-cell responses 6 And cell therapy targeting neoantigens can in some cases cause tumor regression in selected patients. 7 [0004] One question in neoantigen vaccine design is which of the many coding mutations present in a subject's tumor would produce the "...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): A61K39/00A61K39/12A61P31/16A61P31/18A61P31/20A61P31/14C07K14/18C07K14/705C12N15/86
CPCA61K39/12C12N15/86A61K2039/53A61K2039/545A61K2039/585A61K2039/6037A61K2039/605A61K2039/70C12N2710/10343C12N2710/20034C12N2730/10134C12N2740/14034C12N2740/16034C12N2740/16134C12N2740/16234C12N2740/16334C12N2760/14134C12N2760/16134C12N2770/24234C12N2770/36143A61P31/20A61P31/14A61P31/16A61P31/18A61K39/001188A61K39/001191C07K14/70539C07K14/4748C07K2319/60A61P31/12
Inventor W·布莱尔K·朱斯A·R·拉帕波特C·D·斯卡伦L·吉特林
Owner GRITSTONE BIO INC
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products