NEOANTIGEN IDENTIFICATION, MANUFACTURE, AND USE

1 . (canceled)

2 . A method for identifying one or more antigens that are likely to be presented on surface of cells, the method comprising:

obtaining data representing peptide sequences of each of a set of antigens;

determining, using a neural network model stored in a non-transitory computer readable medium of one or more computing devices, a set of presentation likelihoods for the set of antigens, each presentation likelihood in the set representing the likelihood that a corresponding antigen is presented by one or more class I MHC alleles on the surface of the cells, the neural network model comprising:

two or more layers comprising a first layer and a second layer, each layer comprising one or more nodes, wherein said nodes comprise a memory location for one or more input values;

a plurality of connections between nodes of said first layer and one or more nodes of said second layer,

optimized parameters stored in memory locations, wherein the optimized parameters transform input values of nodes of the first layer into input values for nodes of the second layer connected to the nodes of the first layer,

wherein the optimized parameters are generated using a training data set comprising:

training peptide sequences or data derived from training peptide sequences; and

at least one class I MHC allele associated with the training peptide sequences; and

for each of one or more of the training peptide sequences, a label indicating whether the training peptide sequence was presented by the at least one class I MHC allele;

wherein said determining comprises:

forward feeding the data representing peptide sequences of each of a set of antigens, using a computer processor, through nodes of the first layer and the second layer of the neural network model, said forward feeding comprising transforming the data as they are fed from nodes of the first layer to nodes of the second layer using the optimized parameters;

generating, using a computer processor, a set of presentation likelihoods for the set of antigens from the transformed data, each presentation likelihood in the set representing the likelihood that a corresponding antigen is presented by one or more class I MHC alleles on the surface of the cells;

selecting a subset of the set of antigens based on the set of presentation likelihoods to generate a set of selected antigens; and

returning the set of selected antigens.

3 . The method of claim 2 , wherein the peptide sequences comprise peptide sequences having lengths between 8-15 amino acids.

4 . The method of claim 2 , wherein generating the set of presentation likelihoods for the set of antigens comprises:

generating a dependency score for each of the one or more class I MHC alleles, the dependency scores indicating whether the class I MHC alleles will present the antigen based on the particular amino acids at the particular positions of the peptide sequence.

5 . The method of claim 4 , wherein generating the set of presentation likelihoods for the set of antigens further comprises:

transforming the dependency scores to generate a corresponding per-allele likelihood for each class I MHC allele indicating a likelihood that the corresponding class I MHC allele will present the corresponding antigen; and

combining the per-allele likelihoods to generate the presentation likelihood of the antigen.

6 . The method of claim 5 , wherein the transforming the dependency scores models the presentation of the antigen as mutually exclusive across the one or more class I MHC alleles.

7 . The method of claim 6 , wherein generating the set of presentation likelihoods for the set of antigens further 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 antigen as interfering between the one or more class I MHC alleles.

8 . The method of claim 4 , wherein the set of presentation likelihoods are further identified by at least one or more allele noninteracting features, and further comprising:

applying the neural network model to the allele noninteracting features to generate a dependency score for the allele noninteracting features indicating whether the peptide sequence of the corresponding antigen will be presented based on the allele noninteracting features.

9 . The method of claim 8 , further comprising:

combining the dependency score for each class I MHC allele in the one or more class I MHC alleles with the dependency score for the allele noninteracting feature; and

transforming the combined dependency scores for each class I MHC allele to generate a per-allele likelihood for each class I MHC allele indicating a likelihood that the corresponding class I MHC allele will present the corresponding antigen; and

combining the per-allele likelihoods to generate the presentation likelihood.

10 . The method of claim 9 , further comprising:

transforming a combination of the dependency scores for each of the class I MHC alleles and the dependency score for the allele noninteracting features to generate the presentation likelihood.

11 . The method of claim 2 , wherein the one or more class I MHC alleles include two or more class I MHC alleles.

12 . The method of claim 2 , wherein the at least one class I MHC allele includes two or more different types of class I MHC alleles.

13 . The method of claim 2 , wherein the plurality of samples comprise at least one of:

(a) one or more cell lines engineered to express a single MHC class I allele;

(b) one or more cell lines engineered to express a plurality of MHC class I alleles;

(c) one or more human cell lines obtained or derived from a plurality of patients;

(d) fresh or frozen tumor samples obtained from a plurality of patients; and

(e) fresh or frozen tissue samples obtained from a plurality of patients.

14 . The method of claim 2 , wherein the set of presentation likelihoods are further identified by at least expression levels of the one or more class I MHC alleles in a subject, as measured by RNA-seq or mass spectrometry.

15 . The method of claim 2 , wherein the set of numerical likelihoods are further identified by features comprising at least one of:

(a) the C-terminal sequences flanking the antigen encoded peptide sequence within its source protein sequence; and

(b) the N-terminal sequences flanking the antigen encoded peptide sequence within its source protein sequence.

16 . The method of claim 2 , wherein selecting the set of selected antigens comprises selecting antigens that have an increased likelihood of being presented on the cell surface relative to unselected antigens.

17 . The method of claim 2 , further comprising generating an output for constructing a personalized cancer vaccine from the set of selected antigens.

18 . The method of claim 17 , wherein the output for the personalized cancer vaccine comprises at least one peptide sequence or at least one nucleotide sequence encoding the set of selected antigens.

19 . The method of claim 18 , wherein the neural network model exhibits a positive predictive value (PPV) that achieves 0.114 at 10% recall rate.

20 . A system comprising:

a processor configured to execute computer-executable instructions for performing a method comprising:

obtaining data representing peptide sequences of each of a set of antigens;

determining, using a neural network model stored in a non-transitory computer readable medium of one or more computing devices, a set of presentation likelihoods for the set of antigens, each presentation likelihood in the set representing the likelihood that a corresponding antigen is presented by one or more class I MHC alleles on the surface of the cells, the neural network model comprising:

two or more layers comprising a first layer and a second layer, each layer comprising one or more nodes, wherein said nodes comprise a memory location for one or more input values;

a plurality of connections between nodes of said first layer and one or more nodes of said second layer,

optimized parameters stored in memory locations, wherein the optimized parameters transform input values of nodes of the first layer into input values for nodes of the second layer connected to the nodes of the first layer,

wherein the optimized parameters are generated using a training data set comprising:

training peptide sequences or data derived from training peptide sequences; and

at least one class I MHC allele associated with the training peptide sequences; and

for each of one or more of the training peptide sequences, a label indicating whether the training peptide sequence was presented by the at least one class I MHC allele;

wherein said determining comprises:

forward feeding the data representing peptide sequences of each of a set of antigens, using a computer processor, through nodes of the first layer and the second layer of the neural network model, said forward feeding comprising transforming the data as they are fed from nodes of the first layer to nodes of the second layer using the optimized parameters;

generating, using a computer processor, a set of presentation likelihoods for the set of antigens from the transformed data, each presentation likelihood in the set representing the likelihood that a corresponding antigen is presented by one or more class I MHC alleles on the surface of the cells;

selecting a subset of the set of antigens based on the set of presentation likelihoods to generate a set of selected antigens; and

returning the set of selected antigens.

21 . The system of claim 20 , wherein the peptide sequences comprise peptide sequences having lengths between 8-15 amino acids.