Methods and systems for identification and prioritization of mutation-derived neoantigens
Cancer immunology provides promising new avenues for cancer treatment but validation of potential neoantigens to target is costly and expensive. Analysis of MHC binding affinity, antigen processing, similarity to known antigens, predicted expression levels (as mRNA or proteins), self-similarity, and mutant allele frequency, provides screening method to identify and prioritize candidate neoantigens using sequencing data. Methods of the invention thereby save time and money by identifying the priority candidate neoantigens for further experimental validation.
1. A method for prioritizing candidate neoantigens for a patient, the method comprising the steps of:
obtaining a plurality of candidate neoantigens;
determining self-similarity of members of said plurality;
determining similarity of members of said plurality to known antigens;
determining a level of expression of members of said plurality;
identifying a mutant allele frequency in exons encoding members of said plurality;
applying a rule to the results of the determining steps and identifying step to rank members of said plurality according to a likelihood of clinical significance; and
experimentally validating a subset of said plurality of candidate neoantigens based on the ranking.
2. The method of claim 1 , further comprising preparing a report comprising the ranked members of the plurality of candidate neoantigens.
3. The method of claim 2 , wherein one or more of the obtaining, determining, identifying, or applying steps are performed using a computer comprising a processor coupled to a tangible, non-transient memory and an input/output device.
4. The method of claim 3 , further comprising sending the report to the output device.
5. The method of claim 1 , wherein said plurality of candidate neoantigens is derived from a patient tumor sample.
6. The method of claim 1 , wherein said plurality of candidate neoantigens is obtained by determining HLA genotype and MHC binding affinity for candidate peptides obtained from a tumor sample.
7. The method of claim 6 , wherein the HLA genotype and the MHC binding affinity for candidate peptides is determined from in silico from peptide sequence data.
8. The method of claim 6 , wherein the HLA genotype and the MHC binding affinity for candidate peptides are determined by assay.
9. The method of claim 6 , wherein the candidate peptides are obtained by comparing peptides from the tumor sample to corresponding peptides from a normal sample, wherein candidate peptides comprise a mutation relative to the corresponding peptides.
10. The method of claim 6 , wherein the applying step comprises removing candidate neoantigens having a MHC binding affinity of more than 1000 nM from the plurality of candidate neoantigens.
11. The method of claim 6 , wherein the applying step comprises removing candidate neoantigens having a MHC binding affinity of more than 750 nM from the plurality of candidate neoantigens.
12. The method of claim 6 , wherein the applying step comprises removing candidate neoantigens having a MHC binding affinity of more than 500 nM from the plurality of candidate neoantigens.
13. The method of claim 1 , wherein the plurality of candidate neoantigens are each assigned an MHC classification of strong binding (SB) or weak binding (WB) and the applying step comprises ranking the plurality so that SB candidate neoantigens are ranked higher than WB candidate neoantigens.
14. The method of claim 1 , further comprising:
determining antigen peptide processing classification of members of said plurality.
15. The method of claim 14 , wherein the plurality is assigned a classification of epitope (E) or non-antigen (NA) and the applying step comprises ranking the plurality so that E candidate neoantigens are ranked higher than NA candidate neoantigens.
16. The method of claim 14 , wherein the antigen peptide processing classification is determined using a peptide cleavage prediction or a transporter associated with antigen processing (TAP) affinity prediction.
17. The method of claim 1 , wherein the applying step comprises ranking the plurality so that candidate neoantigens with lower self-similarity are ranked higher than neoantigens with higher self-similarity.
18. The method of claim 1 , wherein the level of expression comprises an RNAseq expression value and the applying step comprises removing candidate neoantigens having an expression value below 10 reads per kilobase per million reads mapped (RPKM) from the plurality of candidate neoantigens.
19. The method of claim 1 , wherein the applying step comprises removing candidate neoantigens having an expression value below 25 reads per kilobase per million reads mapped (RPKM) from the plurality of candidate neoantigens.
20. The method of claim 1 , wherein the applying step comprises ranking the plurality based on 100 percent amino acid identity to portions of known antigens so that candidate neoantigens with amino acid identity to longer portions of known antigens are ranked higher than candidate neoantigens with amino acid identity to shorter portions of known antigens.
21. A method for identifying shared neoantigens, the method comprising the steps of:
selecting a plurality of recurrent mutations that occur in more than one tumor type;
determining, based upon predicted peptide sequence, which of said plurality are potential neoantigens;
determining self-similarity of members of said potential neoantigens;
determining similarity of members of said potential neoantigens to known antigens;
determining a level of expression of members of said potential neoantigens;
identifying said potential neoantigens as shared neoantigens based upon prevalence of HLA class I alleles and the prevalence of recurrent mutations across multiple tumor types; and
experimentally validating the shared neoantigens.