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NZ759958B2 - Neoantigen identification, manufacture, and use - Google Patents

Neoantigen identification, manufacture, and use Download PDF

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Publication number
NZ759958B2
NZ759958B2 NZ759958A NZ75995818A NZ759958B2 NZ 759958 B2 NZ759958 B2 NZ 759958B2 NZ 759958 A NZ759958 A NZ 759958A NZ 75995818 A NZ75995818 A NZ 75995818A NZ 759958 B2 NZ759958 B2 NZ 759958B2
Authority
NZ
New Zealand
Prior art keywords
patient
neoantigens
neoantigen
presentation
subset
Prior art date
Application number
NZ759958A
Other versions
NZ759958A (en
Inventor
Sullivan Brendan Bulik
Jennifer Busby
Adnan Derti
Roman Yelensky
Original Assignee
Gritstone Oncology Inc
Filing date
Publication date
Application filed by Gritstone Oncology Inc filed Critical Gritstone Oncology Inc
Priority claimed from PCT/US2018/036571 external-priority patent/WO2018227030A1/en
Publication of NZ759958A publication Critical patent/NZ759958A/en
Publication of NZ759958B2 publication Critical patent/NZ759958B2/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/46—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates
    • C07K14/47—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates from mammals
    • C07K14/4701—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates from mammals not used
    • C07K14/4748—Tumour specific antigens; Tumour rejection antigen precursors [TRAP], e.g. MAGE
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies

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 and methods for obtaining high quality sequencing data from a tumor. Further, described herein are systems and methods for identifying somatic changes in polymorphic genome data. Further, described herein are systems and methods for selecting a subset of patients for treatment. A utility score indicating an estimated number of neoantigens presented on the surface of tumor cells is determined for each patient based on one or more neoantigen candidates identified for the patient. The subset of patients are selected based on the determined utility scores. The selected subset of patients can receive treatment, such as neoantigen vaccines or checkpoint inhibitor therapy. Finally, described herein are unique cancer vaccines.

Claims (17)

1. A method of identifiying a subset of patients for treatment, comprising: obtaining, for each patient, at least one of exome, transcriptome, or whole genome tumor nucleotide sequencing data from tumor cells and normal cells obtained previously from the patient, wherein the tumor nucleotide sequencing data is used to obtain peptide sequences of each of a set of neoantigens identified by comparing the nucleotide sequencing data from the tumor cells and the nucleotide sequencing data from the normal cells, wherein the peptide sequence of each neoantigen for the patient comprises at least one alteration that makes it distinct from a corresponding wild-type parental peptide sequence identified from the normal cells of the patient; generating, for each patient, a set of numerical presentation likelihoods for the set of neoantigens for the patient by inputting the peptide sequences of each of the set of neoantigens into a machine-learned presentation model, each presentation likelihood representing the likelihood that a corresponding neoantigen is presented by one or more MHC alleles on the surface of the tumor cells of the patient, wherein the machine-learned presentation model comprises: a plurality of parameters identified at least based on a training data set comprising: labels obtained by mass spectrometry measuring presence of peptides bound to at least one MHC allele identified as present in at least one of a plurality of samples, training peptide sequences, and at least one MHC allele associated with the training peptide sequences; and a function representing a relation between the peptide sequences and the presentation likelihoods based on the plurality of parameters; identifying, for each patient, one or more neoantigens from the set of neoantigens for the patient, wherein identifying the one or more neoantigens for the patient comprises selecting a subset of neoantigens in the set of neoantigens for the patient and wherein the subset of neoantigens are neoantigens having highest presentation likelihoods in the set of presentation likelihoods for the patient; determining, for each patient, a utility score indicating an estimated number of neoantigens presented on the surface of the tumor cells of the patient as determined by the corresponding presentation likelihoods for the one or more neoantigens for the patient; and selecting the subset of patients for treatment, each patient in the subset of patients associated with a utility score that satisfies a predetermined inclusion criteria.
2. The method of claim 1, further comprising identifying, for each patient in the selected subset of patients, one or more T-cells or T-cell receptors that are antigen-specific for at least one of the one or more neoantigens identified for the patient.
3. The method of claim 1, wherein identifying the one or more neoantigens for the patient comprises selecting the entire set of neoantigens identified for the patient.
4. The method of claim 1, wherein selecting the subset of patients for treatment comprises selecting the subset of patients having tumor mutation burden (TMB) above a minimum threshold, wherein the TMB for a patient indicates a number of neoantigens in the set of neoantigens associated with the patient.
5. The method of claim 1, wherein selecting the subset of patients for treatment comprises selecting the subset of patients having utility scores above a minimum threshold.
6. The method of claim 1, wherein the utility score is a summation of the presentation likelihoods for each neoantigen in the identified subset of neoantigens of the patient.
7. The method of claim 1, wherein the utility score is a probability that a number of presented neoantigens in the identified one or more neoantigens for the patient is above a minimum threhold.
8. The method of claim 1, wherein the training data set further comprises at least one of (a) data associated with peptide-MHC binding affinity measurements for at least one of the peptides; and (b) data associated with peptide-MHC binding stability measurements for at least one of the peptides.
9. The method of claim 1, wherein the set of numerical likelihoods are further identified by features comprising at least one of: (a) the C-terminal sequences flanking the neoantigen peptide sequence within its source protein sequence; and (b) the N-terminal sequences flanking the neoantigen peptide sequence within its source protein sequence.
10. The method of claim 1, wherein the set of presentation likelihoods are further identified by at least expression levels of the one or more MHC alleles in the subject, as measured by RNA-seq or mass spectrometry.
11. The method of claim 1, wherein the set of presentation likelihoods are further identified by features comprising at least one of: (a) predicted affinity between a neoantigen in the set of neoantigens and the one or more MHC alleles; and (b) predicted stability of a neoantigen peptide-MHC complex.
12. The method of claim 1, wherein inputting the peptide sequences of each of the set of neoantigens into the machine-learned presentation model comprises: applying the machine-learned presentation model to the peptide sequence of each neoantigen to generate a dependency score for each of the one or more MHC alleles indicating whether a MHC allele will present the neoantigen based on the particular amino acids at the particular positions of the peptide sequence.
13. The method of claim 12, wherein inputting the peptide sequences into the machine- learned presentation model comprises: transforming the dependency scores to generate a corresponding per-allele likelihood for each MHC allele indicating a likelihood that the corresponding MHC allele will present the corresponding neoantigen; and combining the per-allele likelihoods to generate the presentation likelihood of the neoantigen.
14. The method of claim 13, wherein transforming the dependency scores models the presentation of the neoantigen as mutually exclusive across the one or more class MHC alleles.
15. The method of claim 12, wherein inputting the peptide sequences into the machine- learned presentation model comprises: transforming a combination of the dependency scores to generate the presentation likelihood, wherein transforming the combination of the dependency scores models the presentation of the neoantigen as interfering between the one or more MHC alleles.
16. Use of a neoantigen vaccine in the manufacture of a medicament for treating cancer, wherein the neoantigen vaccine includes at least one of the one or more neoantigens identified for the patient according to any one of claim 1 to 14 and wherein the patient has been selected for treatment according to the method of any one of claims 1 to 14 prior to administration of the neoantigen vaccine.
17. Use of a checkpoint inhibitor in the manufacture of a medicament for treating cancer, wherein the patient has been selected for treatment according to the method of any one of claims 1 to 14 prior to administration of the checkpoint inhibitor.
NZ759958A 2018-06-08 Neoantigen identification, manufacture, and use NZ759958B2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201762517786P 2017-06-09 2017-06-09
PCT/US2018/036571 WO2018227030A1 (en) 2017-06-09 2018-06-08 Neoantigen identification, manufacture, and use

Publications (2)

Publication Number Publication Date
NZ759958A NZ759958A (en) 2024-03-22
NZ759958B2 true NZ759958B2 (en) 2024-06-25

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