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Neoantigen identification, manufacture, and use

A technology of antigens and alleles, which is applied in the direction of cancer antigen components, vertebrate antigen components, and medical preparations containing active ingredients, etc. It can solve the problems of missing candidate neoantigens and inefficient use of autoimmunity in vaccines

Pending Publication Date: 2018-09-28
GRITSTONE BIO INC
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  • 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

Method used

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  • Neoantigen identification, manufacture, and use
  • Neoantigen identification, manufacture, and use
  • Neoantigen identification, manufacture, and use

Examples

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Embodiment 1

[0485] VIII.C.1. Example 1: Maximum of Independent Allele Models

[0486] 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.

[0487]

[0488] VIII.C.2. Example 2.1: The Funciton-of-Sums Model

[0489] In one embodiment, the training module 316 makes peptide p by k The estimated probability of presentation u k Modeling:

[0490]

[0491] where element a h k For...

Embodiment 22

[0501] VIII.C.3. Example 2.2: Functional Model Using the Sum of Allelic Non-Interacting Variables

[0502] 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:

[0503]

[0504] 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.

[0505] Therefore, according to equation (14), the fun...

Embodiment 31

[0516] VIII.C.4. Example 3.1: Models Using Implicit Independent Allele Likelihoods

[0517] In another embodiment, the training module 316 makes the peptide p by k The estimated probability of presentation u k Modeling:

[0518]

[0519] where element a h k For the peptide sequence p k Associated multiple MHC alleles h ∈ H is 1, u' k h is the implicit independent allelic presentation likelihood of the MHC allele h, and the vector v is the element v h corresponds to a h k u' k h , s(·) is a function that maps element v, and r(·) is a clipping function that clips the input values ​​into a given range. As described in more detail below, s(·) may be a sum function or a second order function, but it is understood that in other embodiments, s(·) may be any function, such as a maximum function. The value of the parameter set θ with respect to the likelihood of implicitly independent alleles can be determined by minimizing a loss function with respect to θ, where i is...

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Abstract

Disclosed herein is a system and methods for determining the alleles, neoantigens, and vaccine composition as determined on the basis of an individual's tumor mutations. Also disclosed are systems andmethods for obtaining high quality sequencing data from a tumor. Further, described herein are systems and methods for identifying somatic changes in polymorphic genome data. Finally, described herein are unique cancer vaccines.

Description

[0001] Cross References to Related Applications [0002] This application claims U.S. Provisional Application 62 / 268,333 filed December 16, 2015, U.S. Provisional Application 62 / 317,823, filed April 4, 2016, U.S. Provisional Application 62 / 379,986, filed August 26, 2016, 2016 Benefit and Priority of US Provisional Application 62 / 394,074, filed September 13, and US Provisional Application 62 / 425,995, filed November 23, 2016, each incorporated by reference in its entirety for all purposes. Background technique [0003] Therapeutic vaccines based on tumor-specific neoantigens hold great promise as a new 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 cell the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61K9/10A61K35/15A61K39/00A61K39/39A61K39/395A61P35/00G16H20/10
CPCA61K35/15A61K39/0011G16B20/00G16B40/10A61P35/00A61P35/02G01N33/57484G01N2333/70539G01N33/6848G16B30/00G16H20/10Y02A90/10A61K2039/5154A61K2039/5152A61K2039/577A61K2039/53A61K39/39A61K2039/585G09F19/16
Inventor R·耶冷斯凯A·德里蒂B·布里克-沙利文J·巴斯比
Owner GRITSTONE BIO INC
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