PATENT CLAIM ANALYSIS

Application Number: 16128421
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-01
Patent Classification: ["424", "277100"]

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. Finally, described herein are unique cancer vaccines.

Claim (Index 21):
A system comprising:\n a processor configured to execute computer-executable instructions for performing a method comprising:\n obtaining at least one of exome, transcriptome, or whole genome nucleotide sequencing data from the tumor cells and normal cells of the subject, wherein the nucleotide sequencing data is used to obtain data representing 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 comprises at least one alteration that makes it distinct from the corresponding wild-type peptide sequence identified from the normal cells of the subject; \n encoding the peptide sequences of each of the neoantigens into a corresponding numerical vector, each numerical vector including information regarding a plurality of amino acids that make up the peptide sequence and a set of positions of the amino acids in the peptide sequence; \n inputting the numerical vectors, using a computer processor, into a deep learning presentation model to generate a set of presentation likelihoods for the set of neoantigens, each presentation likelihood in the set representing the likelihood that a corresponding neoantigen is presented by one or more class I MHC alleles on the surface of the tumor cells of the subject, the deep learning presentation model comprising: \n a plurality of parameters identified at least based on a training data set comprising:\n labels obtained by mass spectrometry measuring presence of peptides bound to at least one class I MHC allele identified as present in at least one of a plurality of samples; and \n training peptide sequences encoded as numerical vectors including information regarding a plurality of amino acids that make up the peptide sequence and a set of positions of the amino acids in the peptide sequence; and \n at least one HLA allele associated with the training peptide sequences; and \n \n a function representing a relation between the numerical vector received as input and the presentation likelihood generated as output based on the numerical vector and the parameters, \n the presentation model having a positive predictive value that achieves 0.114 at 10% recall rate; \n selecting a subset of the set of neoantigens based on the set of presentation likelihoods to generate a set of selected neoantigens; and returning the set of selected neoantigens.

Metadata:
- Claim Count in Document: 29.0
- Percentile: 97.0
- Lexical Diversity: 1.47917
- Patent Class: 424.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15466729', '15210489', '15170919', '13640989', '15791301']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.738727346942496
- 35 USC 102 Novelty (BERT): 0.6601908360051012
- Combined Prediction Score: 0.7308736958487565
- Mean Citation Score: 388.519416
- Max Citation Score: 745.2901
- Similarity Product: 603.7927967952013

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test