EP-4742249-A1 - METHOD OF SELECTING NEOANTIGEN FOR DEVELOPMENT OF PERSONALIZED CANCER VACCINE

Abstract

Provided are a method of selecting a tumor-specific neoantigen (immunogenic peptide), and a use of the selected tumor-specific neoantigen for preparing a personalized cancer vaccine.

Inventors

• JEONG, Seihwan
• CHOE, Kyuhong
• KIM, Seunghae
• KIM, YOUNGMOK
• YEU, Yunku
• KIM, DA EUN
• KIM, Hyeongsu

Assignees

• LG Chem, Ltd.

Dates

Publication Date

20260513

Application Date

20240823

Claims (12)

1. A method for selecting a tumor-specific immunogenic peptide derived from a patient, the method comprising the steps of: (1) obtaining NGS sequence information from tumor cells or tumor tissues and normal cells or normal tissues derived from a patient; (2) obtaining tumor-specific peptide sequence information of at least 7 amino acids in length, having one or more amino acid variations in the NGS sequence of the tumor cells or tumor tissues compared to the NGS sequence of the normal cells or normal tissues; (3) measuring an expression level (TPM; Transcripts per Million) of the tumor-specific peptide or the gene encoding the tumor-specific peptide in the tumor cells or tumor tissues; (4) applying the tumor-specific peptide sequence information to a binding affinity prediction model and determining a binding affinity score (BA) to an HLA molecule; (5) applying the tumor-specific peptide sequence information to an immunogenicity prediction model and determining an immunogenicity score (IMM); (6) determining a priority rank of a tumor-related peptide based on the following formula: Neoantigen prioritization score = min(TPM, 1) * (W1*BA + W2*IMM)/(W1+W2) [0<W1<1, 0<W2<1]; and (7) selecting two or more tumor-specific immunogenic peptides based on the determined priority rank of the tumor-specific peptides.
2. The method of claim 1, further comprising the steps of: (a) obtaining peptide sequence information derived from tumor cells or tumor tissues and normal cells or normal tissues from a peptide database; (b) obtaining class I HLA peptide sequence information from a biological sequence database; (c) constructing a binding affinity prediction model by training the data obtained in steps (a) and (b) on a model capable of predicting a binding affinity score (BA) for HLA, and obtaining a binding affinity score; and (d) constructing an immunogenicity prediction model by training the data obtained in steps (a) and (b) on a model capable of predicting an immunogenicity score (IMM), and obtaining an immunogenicity score.
3. The method of claim 2, wherein the peptide database used for the construction of the prediction model in step (a) undergoes one or more selection processes of the following: i) deleting data not corresponding to HLA-A, HLA-B, or HLA-C, ii) deleting data with mutations in HLA molecules, iii) deleting data with peptide lengths of 7 or less or 15 or more, and iv) removing amino acid sequences containing amino acid residues other than the 20 human-derived amino acids.
4. The method of claims 1 to 3, wherein the HLA comprises different HLA types or different HLA alleles.
5. The method of any one of claims 1 to 3, wherein the training in steps (c) and (d) is performed by combining one or more artificial intelligence models and one or more training methods.
6. The method of any one of claims 1 to 3, wherein the binding affinity score is determined by an IC50 value or Kd value related to binding with HLA.
7. The method of any one of claims 1 to 3, wherein the immunogenicity score is determined by one or more pieces of information selected from the group consisting of the cytokine secretion from T-cell, T-cell proliferation due to immunogenic activation, chemokine secretion, and the cytotoxicity of activated T-cell, when the candidate peptide is treated with immune cells or a sample containing the immune cells.
8. The method of claim 7, wherein the sample containing the immune cells is selected from the group consisting of blood, white blood cells, and peripheral blood mononuclear cells (PBMC), and the information is selected from at least two of the group consisting of IFN-g, IL-2, TNF-a, CCL4, CXCL9, and granzyme B.
9. The method of any one of claims 1 to 3, wherein the expression level of the candidate peptide or a coding gene therefor in the patient-derived sample in step (3) is obtained through RNA sequencing from the patient sample data.
10. The method of any one of claims 1 to 3, wherein the tumor-specific immunogenic peptide derived from the patient is used for production of a personalized cancer vaccine for the patient.
11. An anti-cancer vaccine composition comprising: the tumor-specific immunogenic peptide selected by the method of any one of claims 1 to 3 or a nucleic acid molecule encoding the peptide; and a pharmaceutically acceptable carrier.
12. A method of preparing an anti-cancer vaccine, the method comprising a step of mixing: the tumor-specific immunogenic peptide selected by the method of any one of claims 1 to 3 or a nucleic acid molecule encoding the peptide; and a pharmaceutically acceptable carrier.

Description

[Technical Field] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority and priority to Korean Patent Application No. 10-2023-0110887, filed on August 23, 2023, with the Korean Intellectual Property Office, the disclosures of which are incorporated herein by reference. The present disclosure provides a method of selecting neoantigens (tumor-specific immunogenic peptides) and the use of the selected tumor-associated neoantigens for the development of personalized cancer vaccines. [Background] The term "neoantigen", as used herein, refers to an antigen generated by cancer-specific mutations and, more specifically, to a mutated peptide expressed on the cell surface in a cancer-specific manner that can be recognized as an antigen, including epitopes (surface proteins). Since neoantigens are expressed exclusively in cancer cells, they can be applied to the development of targeted cancer therapies. In the present disclosure, neoantigens are a type of immunogenic peptides and may also be referred to as "tumor-specific neoantigens", "tumor-specific antigens", or "tumor-specific immunogenic peptides". The term "personalized cancer vaccine" refers to a therapeutic agent or a treatment method in which neoantigens with immune amplification capabilities are selected from cancer-specific antigens expressed in individual patients, produced in the form of proteins or mRNA, and delivered to the patient using lipid-based nanoparticles such as lipid nanoparticles (LNP, Solid Lipid Nanoparticles (SLN), Nanostructured Lipid Carriers (NLC), etc.) or liposome-nucleic acid complexes (e.g., lipoplex, liposomes), activating the patient's immune system to eliminate cancer cells. In order to mediate the efficacy of personalized cancer vaccines, the key factor is discovering the set of neoantigens specific to each individual patient and selecting and prioritizing immunogenic neoantigens. Immunogenic neoantigens must have a binding affinity with Human Leukocyte Antigen (HLA), which regulates immune responses such as cellular and humoral immunity in response to the antigen stimulation. When the specific neoantigen sequences, that are bound HLA, are recognized by T cell receptors in the body, immune activation against the neoantigens occurs. However, there are challenges in discovering immunogenic neoantigens applicable to the development of personalized cancer vaccines, as the cancer-specific mutations vary for each patient, and the HLA type expressed differs among individuals. Moreover, the collection and selection of data for training neoantigen Al prediction models that can predict immunogenic neoantigens, as well as the establishment of methods for selecting neoantigens applicable to individual patients in personalized cancer vaccine development, are lacking. This makes it difficult to efficiently utilize neoantigen prediction models. Conventionally, Al prediction models have been developed to measure the binding affinity between immunogenic peptides, which include mutations in cancer cells of patients, and HLA to select neoantigens expected to have anti-cancer efficacy when applied to cancer vaccines. However, it has been difficult to use the derived immunogenic peptides as vaccines, as a high binding affinity between patient epitopes and HLA does not necessarily lead to a strong T cell immune response. Additionally, technologies have been developed to predict T cell immunogenicity by comparing the physicochemical properties of amino acids between peptides containing mutations in cancer cells and nonmutated peptides in non-cancer cells. However, this method, which relies only on amino acid sequence information, fails to reflect the three-dimensional protein structure. As a result, the prediction success rate of immune responses is not high because the HLA-peptide complex, which includes the neoantigen peptides with mutations in cancer cells, must be comprehensively recognized by the T cell receptor to trigger an immune response, and binding affinity alone cannot encompass the diverse immune responses. [Disclosure] [Technical Problem] One aspect of the present application provides a method for selecting a tumor-specific immunogenic peptide (neoantigen, tumor-specific antigen) (including a prediction method, a selection method, an identification method, or a method for providing information for selection). The method for selecting a tumor-specific immunogenic peptide may include the steps of: (I) constructing an immunogenic peptide prediction model; and (II) predicting and/or selecting a tumor-specific immunogenic peptide. The step (I) of constructing an immunogenic peptide prediction model may include the following steps (a) to (d): (a) obtaining peptide information from a peptide database, wherein the peptide information may include one or more pieces of information selected from sequence information of peptides derived from tumor cells or tumor tissues, amino acid sequence information of peptides d