KR-20260068018-A - Calculation method for selecting personalized neoantigen vaccines

Abstract

A system and method for identifying a personalized tumor vaccine are provided. A somatic variant for a subject is determined, and an RNA sequence read is obtained from the subject's tumor. From the RNA sequence read, a fusion protein is determined, and each fusion protein is a fusion of a portion of a first protein and a portion of a second protein. Candidate neoantigens are selected, comprising a first subset of candidate neoantigens encoding the somatic variant and a second subset of candidate neoantigens encoding residues of the first and second protein portions. A score for each candidate neoantigen is determined using a scoring function that includes a first scoring term that upweights candidate neoantigens having higher relative class I MHC affinities, taking into account the subject's class I HLA type. Based on each score, two or more candidate neoantigens are selected as a final neoantigen set for the tumor vaccine.

Inventors

• 오도넬, 팀
• 코디쉬, 줄리아
• 바르드와즈, 니나

Assignees

• 이칸 스쿨 오브 메디슨 엣 마운트 시나이

Dates

Publication Date

20260513

Application Date

20240412

Priority Date

20230412

Claims (20)

1. As a method for identifying a personalized tumor vaccine for human subjects with cancer, (A) A step of determining a first plurality of somatic variants of the above-mentioned object; (B) A step of obtaining a first plurality of sequence reads from RNA molecules of a tumor sample obtained from the above-mentioned subject; (C) A step of determining one or more fusion proteins encoded by the first plurality of sequence reads from the first plurality of sequence reads, wherein each of the one or more fusion proteins is a fusion of a portion of each first human protein and a portion of each second human protein; (D) A step of selecting a plurality of candidate neoantigens including a first subset of candidate neoantigens and a second subset of candidate neoantigens, Each neoantigen in the first subset of the above-mentioned candidate neoantigens encodes a somatic variant among the above-mentioned first plurality of somatic variants, and Each neoantigen in the second subset of the candidate neoantigens encodes one or more residues from each of the first human protein portions and one or more residues from each of the second human protein portions; (E) determining a score for each candidate neoantigen among the plurality of candidate neoantigens using a scoring function comprising a first scoring term that upweights each candidate neoantigen having a higher class I MHC affinity compared to each candidate neoantigen having a lower class I major histocompatibility complex (MHC) affinity, taking into account the class I human leukocyte antigen (HLA) type of the human subject; and (F) A method comprising the step of selecting two or more candidate neoantigens among the plurality of candidate neoantigens as a final neoantigen set for the tumor vaccine based on the respective scores of each candidate neoantigen among the plurality of candidate neoantigens.
2. In paragraph 1, the above method The method further includes the step of determining the respective allele-specific expression of each somatic variant among the first plurality of variants using the first plurality of sequence reads; A method comprising a second scoring term that upweights each candidate neoantigen representing an allele having a higher abundance in the first plurality of sequence reads compared to each candidate neoantigen representing an allele having a lower abundance in the first plurality of sequence reads.
3. In claim 1, the step of determining the first plurality of somatic variants of the object is, A step of obtaining a second plurality of sequence reads from DNA molecules of a tumor sample obtained from the above-mentioned subject; A step of obtaining a third plurality of sequence leads from DNA molecules of a tumor sample obtained from a subject; and A method comprising the step of identifying the first plurality of somatic variants of the subject by using the second plurality of sequence leads and the third plurality of sequence leads, wherein the somatic variant observed in the second plurality of sequence leads and not observed in the third plurality of sequence leads is included in the first plurality of somatic variants.
4. In paragraph 3, A step of obtaining the second plurality of sequence reads by performing a first exome sequencing analysis with at least 100x coverage; and A method further comprising the step of obtaining the second plurality of sequence reads by performing a second exome sequencing analysis with at least 100x coverage.
5. A method according to claim 3 or 4, wherein the normal sample is a tissue sample located near the original location of the tumor within the subject.
6. A method according to paragraph 3 or 4, wherein the normal sample is a blood sample.
7. A method according to any one of claims 1 to 6, wherein the cancer is glioblastoma or prostate cancer.
8. A method according to any one of claims 1 to 7, wherein the cancer is a carcinoma, melanoma, lymphoma/leukemia, sarcoma, or glial cell tumor.
9. A method according to any one of claims 1 to 8, wherein the cancer is lung cancer, pancreatic cancer, colon cancer, stomach cancer or esophageal cancer, breast cancer, ovarian cancer, prostate cancer, or liver cancer.
10. A method according to any one of claims 1 to 9, wherein each of the first plurality of somatic variants is a single nucleotide variant or an indel mutant.
11. A method according to any one of claims 1 to 10, wherein the sample of the tumor is a fresh frozen sample.
12. A method according to any one of claims 1 to 11, wherein each of the plurality of candidate novel antigens has a length of N residues, and N is 8, 9, 10, 11, 12, 13, or 14.
13. A method according to any one of claims 1 to 11, wherein each of the plurality of candidate neoantigens has a length of 9 residues.
14. A method according to any one of claims 1 to 13, further comprising the step of excluding candidate neoantigens comprising mutations from wild-type sequences at N-terminal residue positions from the plurality of candidate neoantigens or from the final set of candidate neoantigens.
15. A method comprising, in any one of claims 1 to 14, further the step of determining the class I HLA type of the human subject using the first plurality of sequence analysis reads.
16. A method according to any one of claims 1 to 14, further comprising the step of determining the class I HLA type of the human subject using a polymerase chain reaction with a biological sample from the cancer subject.
17. In any one of paragraphs 1 to 16, the method The method further includes the step of determining the hydrophobicity of each candidate neoantigen among the plurality of candidate neoantigens mentioned above; The above scoring function further includes a scoring term for the hydrophobicity of each candidate neoantigen, a method.
18. A method according to claim 17, wherein the step of determining the hydrophobicity of each of the plurality of candidate neoantigens comprises assigning the maximum hydrophobicity score of any heptmer within each candidate neoantigen to each candidate neoantigen.
19. A method according to claim 17 or 18, wherein the scoring term for the hydrophobicity of each candidate neoantigen is to upweight the candidate neoantigen that is more hydrophobic compared to the candidate neoantigen that is less hydrophobic.
20. A method according to any one of claims 1 to 19, wherein the scoring function further comprises a term for the class II MHC affinity of each candidate neoantigen, which upweights each candidate neoantigen having a higher class II MHC affinity than each candidate neoantigen having a lower class II MHC affinity, taking into account the class II HLA type of the human subject.

Description

Calculation method for selecting personalized neoantigen vaccines Cross-reference of related applications This application claims priority to U.S. Provisional Patent Application No. 63/495,637 filed on April 12, 2023, the entirety of which is incorporated herein by reference. electronically submittedReference to sequence list The present application includes a sequence list in XML format submitted electronically with the present application via the EFS-Web. The XML copy created on April 12, 2024, is named "1045935046WO_SL.xml" and has a size of 22,000 bytes. The sequence list is incorporated herein by reference in its entirety. Technology field The present disclosure generally relates to a system and method for identifying a tumor vaccine personalized for a human subject with cancer, wherein the tumor vaccine comprises a neoantigen. Cancer-specific neoantigens, resulting from genetic changes accumulated by tumor cells, encode novel amino acid stretches that are not present in the normal genome. Consequently, these tumor-specific peptides are not negatively selected by the immune system as part of the "auto" proteome. Therefore, the total neoantigen load inferred from in silico analysis of whole-exome sequencing data from a patient's tumor can be used not only as a predictor of a positive response to immunotherapy but also for vaccine designs, including but not limited to peptide-based vaccines, RNA vaccines, and/or DNA vaccine designs (Luksza, M. et al. Nature 551, 517-520 (2017)]; Balachandran, VP et al. Nature 551, S12-S16 (2017)]; Charoentong, P. et al. Cell Reports 18, 248-262 (2017)]; Hugo, W. et al. Cell 165, 35-44 (2016)]). Neoantigen vaccines are developed by comparing the genotype of tumor cells to the patient's matching normal tissue or blood. Next, the collected somatic missense and frameshift mutations are converted into corresponding tumor-specific peptides, which are then screened for MHC-I epitopes through experimental functional tests or the execution of in silico prediction algorithms. Several algorithms exist to optimize the prediction of in silico epitope-HLA interactions, enabling the prediction of MHC class I and, to a lesser extent, MHC class II tumor neoepitopes. Alternatively, mass spectrometry-based approaches for predicting tumor epitopes also currently exist. Subsequently, these epitopes can be used for short and long peptide-based vaccines, dendritic cell (DC)-based vaccination enhancement, priming of adoptive autologous T cell transfers, and genetically modified cell therapies (Branca, MA Nat. Biotechnol. 34, 1019-1024 (2016)). In this regard, several research groups have reported encouraging results for neoantigen-based cancer vaccines that generate tumor antigen-specific immune responses in both mouse models and clinical trials. Furthermore, both the quantity and quality of neoantigens have been shown to be predictive of clinical outcomes in checkpoint block immunotherapy for specific tumor types. Recognition of neoantigens through vaccination or adoptive T-cell therapy may hold unprecedented potential to advance cancer immunotherapy when combined with other approaches (Reference [Roudko V. et al. Front. Immunol. 11:27, 1-11 (2020)]). Given the above background, systems and methods to improve the treatment of human cancer are required in the industry. Additionally, given the above background, there is a need to develop tumor vaccines that utilize tumor-associated antigens or neoantigens to enhance immune responses. This disclosure addresses the need in the art for systems and methods to determine personalized treatment for human cancer. One of the barriers to be overcome in cancer treatment is the immunosuppressive tumor microenvironment (see, for example, the literature [Quail DF, Joyce JA. Cancer Cell. 2017;31(3):326-341], the entirety of which is incorporated herein by reference). Tumor mutations lead to the formation of tumor-specific antigens called neoantigens, which are not present in normal tissues but are recognized by the immune system to provide specific targets for anti-tumor therapy. Neoantigen vaccines are a novel tool for treating cancer patients. As expected, they can play a crucial role in immunomodulation by amplifying the endogenous repertoire of tumor-specific T cells to eliminate any remaining tumor cells. In general, such therapeutic approaches based on the manipulation of the adaptive immune system have a relatively low risk of toxicity due to the inherent specificity of the induced immune response and thus have the potential to have a favorable side effect profile; In addition, these immune-based therapies have the potential to promote long-term disease remission due to the introduction and persistence of an activated immune cell population in the patient after the treatment process. Several groups have eliminated tumors in murine models using therapeutic vaccines targeting neoantigens. Consequently, many human neoantigen vaccine trials have been initia