Methods for generating personalized neoantigens for a patient's tumor - Patents.com

Abstract

A method for generating a plurality of neoantigens from a sample obtained from a patient, comprising the steps of: - sequencing the DNA and / or RNA from the sample; - identifying a number of DNA and / or RNA variants from the sequencing data; - identifying from said plurality of DNA and / or RNA variants those which give rise to qualitative differences in the corresponding encoded peptides and / or those which give rise to the generation of novel peptide sequences; and generating said plurality of neoantigens, wherein each neoantigen contains one of said identified differences in said encoded peptide.

Description

[Technical field]

[0001] The present invention relates to genome-linked analysis to identify a patient's personalized neoantigens. In particular, the present invention relates to the discovery of a process for generating multiple neoantigens in a patient's tumor. More specifically, the present invention relates to pools of DNA sequences encoding multiple neoantigens.

[0002] In oncology, personalized vaccines for treating tumors, for example personalized therapeutic vaccines that contain tumor-specific antigens (neoantigens) of a patient's tumor, are very promising as the next generation of personalized cancer immunotherapy. [Background technology]

[0003] The concept of personalized cancer vaccination is based on the identification of neoantigens that are able to induce an appropriate immunological response, which allows to generate optimal personalized vaccines, specifically enhancing the patient's immune system in attacking the tumor. In this context, several methods based on algorithmic predictions have been developed to identify neoantigens that are the best candidates for personalized cancer vaccines.

[0004] US Patent Publication No. 2009 / 0133963 provides an optimized approach for identifying and selecting neoantigens for personalized cancer vaccines, T cell therapy, or both. Optimized tumor exome and transcriptome analysis approaches for identifying neoantigen candidates using next generation sequencing (NGS) are addressed. These approaches include trained statistical regression or nonlinear deep learning models configured to predict the presentation of peptides of multiple lengths on a pan-allelic basis, depending on the embodiment.

[0005] This model allows for more reliable prediction of peptide presentation and enables more time- and cost-effective identification of neoantigen- or tumor antigen-specific T cells for personalized therapy using a clinically practical process that uses limited volumes of patient peripheral blood, screens only a few peptides per patient, and does not necessarily rely on MHC multimers.

[0006] However, the predictive algorithms developed in this literature still identify a significant number of false-positive neoantigens that do not elicit an appropriate immunological response in patients.

[0007] Patent Document 2 provides a computer system for making nucleic acid cancer vaccines with maximum cancer efficacy for a given length. This document provides a cancer vaccine that includes one or more nucleic acids that have maximum anti-cancer efficacy for a given length and can cause the body's cellular machinery to produce almost any cancer protein or its fragment of interest. The maximized anti-cancer efficacy can be determined by identifying T cell activation value or survival value.

[0008] US Patent No. 5,999,943 describes a method for identifying and selecting neoantigens from patient tumors. The method described herein targets the identification of complex genomic rearrangements that may exhibit high immunogenic potential, according to the present specification.

[0009] US Patent No. 5,999,633 describes a method for determining neoantigens that have a high probability of being immunogenic, i.e., that have a high probability of being presented by one or more MHC alleles on the surface of a patient's tumor cells. The method describes the in silico selection of neoantigens to be included in a vaccine that are predicted to be optimal for the patient's immunogenic response.

[0010] Currently, all methods for identifying multiple neoantigens to be incorporated into personalized vaccine rests are, at least in part, predictive algorithms that attempt to identify the best neoantigen candidates that induce an appropriate immunological response in patients. Unfortunately, although these documents describe the advantages of those techniques, currently no methods exist that allow the identification of neoantigens that induce an appropriate immunological response with a high level of success. Indeed, despite the use of predictive algorithms, only a small fraction of identified neoantigens (4-20%) induce an immunological response detectable by the apparition of neoantigen-reactive T cells in patients.

[0011] The inventors believe that the methods known in the state of the art for identifying the best neoantigen candidates are biased, because they are based on affinity data between at least neoantigen peptides and HLA receptors that are only available for a specific type of HLA receptor. Thus, the current methods for identifying the best neoantigen candidates may reach some efficacy for patients with said specific type of HLA receptor, while this method is inefficient for the majority of patients with different specific types of HLA receptor. On the other hand, the methods in the art represent an optimum in some ways, since the use of predictive algorithms is believed to result in increased responses. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] EP3759131 Publication Publication [Patent Document 2] EP3813848 Publication Publication [Patent Document 3] WO2021 / 172990 Publication [Patent Document 4] US2020 / 0105377 Publication Summary of the Invention [Problem to be solved by the invention]

[0013] Therefore, the process for identifying neoantigens from tumor biopsy samples needs to be tailored so that they have affinity for the patient's HLA receptor type in order to elicit an appropriate T cell response. [Means for solving the problem]

[0014] To achieve this, the inventors have developed a process for identifying multiple neoantigens in a patient that, when incorporated into a personalized vaccine for the patient, will provide an immunological response, regardless of the patient's HLA receptor type, avoiding the pitfalls and biased results associated with designed algorithms. The present invention provides a process for generating multiple neoantigens from a sample obtained in a patient, comprising the steps of: - sequencing the DNA and / or RNA of said sample; - identifying a number of DNA and / or RNA variants from the sequencing data; - identifying from said plurality of DNA and / or RNA variants those which cause at least one qualitative difference at the peptide level, e.g. in the corresponding encoded peptide (mismatch mutation, frameshift, chromosomal rearrangement, alternative RNA splicing) and / or which cause the generation of a novel peptide sequence encoded from an open reading frame, said open reading frame being present only in the tumor; and generating said plurality of neoantigens, each neoantigen comprising at least one of the identified differences at the peptide level, e.g. (i) one of the identified differences in the encoded peptide and amino acid(s) upstream and / or downstream of said identified difference to form the neoantigen, or (ii) at least a portion of said novel peptide sequence to form the neoantigen.

[0015] Indeed, in the method according to the invention, the multiple neoantigens correspond to multiple DNA variants that cause (i) qualitative differences in the corresponding encoded peptides, or (ii) generation of novel peptides, or the multiple neoantigens are a selection of many (e.g., more than 50, more than 60, more than 100) tumor neoantigens, if not all, identified in the patient, independent of the patient's HLA receptor type. As a result, the method according to the invention provides multiple neoantigens that can elicit an immunological response in the patient when incorporated into a personalized vaccine.

[0016] According to the present invention, an immunological response in a patient refers to the production of specific T cells that target neoantigens.

[0017] The present invention also relates to a pool of DNA and / or RNA sequences encoding said multiple neoantigens obtainable by the method according to the invention.

[0018] Other characteristics and advantages of the invention emerge from the non-limiting following description and by reference to the drawings and examples.

[0019] The present inventors have developed a method for generating multiple neoantigens from a sample obtained in a patient, the method comprising the steps of: - sequencing DNA from the sample; - identifying a number of DNA and / or RNA variants from the sequencing data; - identifying from said plurality of DNA and / or RNA variants those which, for example, give rise to qualitative differences at the peptide level in the corresponding encoded peptides and / or those which give rise to the generation of novel peptide sequences encoded from open reading frames, said open reading frames being present only in the tumor; and generating said plurality of neoantigens, each neoantigen (i) comprising at least one of said identified differences in the encoded peptide and amino acid(s) upstream and / or downstream of said identified differences to form the neoantigen, or (ii) comprising at least a portion of said novel peptide sequence to form the neoantigen.

[0020] According to the present invention, the multiple neoantigens correspond to (i) multiple DNA variants that cause qualitative differences in the corresponding encoded peptides and / or cause the generation of novel peptide sequences encoded from open reading frames, where said open reading frames are present only in the tumor, or (ii) a selection from all tumor neoantigens identified in the patient, where this selection is preferably independent of the patient's HLA receptor type.

[0021] According to the present invention, the term "neoantigen" refers to a tumor-specific antigen of a patient, wherein the tumor-specific antigen is defined by an amino acid sequence that has at least a qualitative difference compared to the amino acid sequence of a corresponding encoded peptide, or wherein the tumor-specific antigen is defined by an amino acid sequence that corresponds to a novel peptide sequence encoded from an open reading frame, wherein said open reading frame is present only in the tumor and not in normal cells of the patient.

[0022] Preferably, the independent selection of multiple neoantigens is a (random) selection of at least 10%, preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% of all tumor neoantigens identified in the patient. Indeed, among such multiple independently selected neoantigens, based on the selection of all neoantigens identified in the patient, some neoantigens have affinity for the patient's HLA receptor type and induce an immunological response defined by the appearance of T cells responding to the neoantigens.

[0023] According to the present invention, the sample from the patient may be derived from a solid and / or liquid biopsy of the patient, preferably a solid biopsy.

[0024] According to the present invention, the sample comprises DNA and / or RNA from at least one tumor cell of the patient. Preferably, the sample further comprises DNA and / or RNA from a blood sample of the patient (e.g., peripheral blood mononuclear cells (PBMC)). More preferably, the sample comprises a first tumor sample comprising DNA and / or RNA from at least one tumor cell of the patient and a second normal sample comprising DNA and / or RNA from a blood sample of the patient and / or DNA and / or RNA from a matched normal tissue of the patient.

[0025] Preferably, the first tumor sample comprises DNA and RNA from at least one tumor cell of the patient.The first tumor sample and the second normal sample are separate samples.According to the present invention, the first tumor sample comprising DNA from at least one tumor cell of the patient and the first tumor sample comprising RNA from at least one tumor cell of the patient can be separate samples.

[0026] More preferably, the first tumor sample comprising DNA from at least one tumor cell of patient and the first tumor sample comprising RNA from at least one tumor cell of patient are the same sample.DNA sequencing can be carried out by any sequencing method, such as high-throughput sequencing, pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, RNA-Seq (Illumina), digital gene expression (Helicos), next-generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), massively parallel sequencing, clonal single molecule array (Solexa), shotgun sequencing, Maxam-Gilbert or Sanger sequencing, primer walking, sequencing using PacBio, SOLID, IonTorrent or Nanopore platform, and any other sequencing method known in the art.

[0027] Preferably, DNA sequencing is performed by next generation sequencing. Next generation sequencing corresponds to a high throughput, massively parallel, DNA sequencing approach. Advantageously, DNA sequencing is performed on a NextSeq500 / 550 or NovaSeq sequencer or any Illumina or MGI sequencer.

[0028] The patient's total DNA, exome or specific target DNA can be sequenced. Preferably, the patient's total DNA is sequenced. Preferably, the first tumor sample and the second normal sample's total DNA are sequenced. More preferably, the first tumor sample and the second normal sample's total DNA and the first tumor sample's RNA are sequenced.

[0029] Advantageously, the DNA sequencing is single cell DNA sequencing of at least 5 different tumour cells, preferably at least 20 different tumour cells, most preferably at least 100 different tumour cells, preferably at least 1000 different tumour cells of the patient.

[0030] Indeed, depending on the tumor type and / or depending on the patient's tumor, tumors may be characterized by a high level of intratumoral heterogeneity, and mutations in tumor cells may differ from those in other tumor cells.

[0031] As a result, single-cell DNA sequencing of tumors allows the identification of more neoantigens and may therefore be very useful in preventing clonal resistance to personalized vaccines that code for multiple neoantigens in patients. More preferably, the identified multiple neoantigens are derived from two or more tumor cells.

[0032] Advantageously, the method according to the invention for identifying multiple neoantigens is carried out more than once, preferably at different stages of tumor evolution and / or before and after tumor treatment. Indeed, during tumor evolution, the mutations that define the tumor may evolve to the point where some mutations disappear while others may appear.

[0033] As a result, the tumor is able to escape attack by specific T cells that target neoantigens. Preferably, the multiple neoantigens identified by the process according to the invention correspond to neoantigens identified in a recent sample taken from the patient, the sample comprising DNA from at least one tumor cell of the patient.

[0034] According to the invention, sequencing the DNA from the sample comprises sequencing the DNA from a first tumor sample. Preferably, sequencing the DNA from the sample comprises sequencing the DNA from the first tumor sample and the DNA from a second normal sample.

[0035] According to the present invention, DNA variants are identified by comparing sequencing data from a first tumor sample with sequencing data from a second normal sample.

[0036] According to another version, in the present invention, identifying a large number of DNA variants from sequenced data is carried out by comparing sequenced data with a reference genome.Preferably, the reference genome is human genome.More preferably, the nucleotide sequence of human genome is obtained from public databases such as NCBI's hg19, i.e. grch37.

[0037] However, to determine the individualized DNA variants of a patient's tumor, it is advantageous to compare the sequenced data from the tumor with corresponding sequenced data from the patient's normal tissues and / or cells, i.e., PBMCs.

[0038] Preferably, the DNA variants may be DNA variants known to be frequently present in a patient's particular tumor type and / or individualized DNA variants identified in the patient's tumor. The DNA variants may be driver mutations or "passenger mutations."

[0039] Driver mutations are mutations that cause tumor initiation and / or development, whereas passenger mutations are mutations that accumulate during tumor progression but do not show a direct role in the tumor progression process.

[0040] The DNA variant may be a point mutation and / or a single nucleotide polymorphism and / or a translocation and / or a frameshift and / or an indel and / or a deletion and / or a fusion (of two different nucleotide segments; i.e. a translocation event).

[0041] The DNA variants can be present in exonic and / or intronic regions and / or silenced portions of the sequenced DNA. According to the present invention, the DNA variants can be present in an open reading frame, said open reading frame being present in the DNA from the first tumor sample and in the DNA from the second normal sample.

[0042] The DNA variant may also be present in a new open reading frame, wherein said new open reading frame is present only in a first tumor sample, and wherein said new open reading frame corresponds to a region of DNA of a second normal sample that is incapable of translation into a peptide (no promoter region, no start codon, premature stop codon).

[0043] Preferably, DNA sequencing and RNA sequencing (in the first tumor sample) are performed on DNA and RNA from the same sample.

[0044] Preferably, the qualitative difference in the encoded peptide is determined by identification of an open reading frame in which an mRNA is identified from RNA sequencing data from a tumor, wherein the mRNA has (i) a predicted peptide sequence having at least one different amino acid compared to a reference peptide sequence, and / or (ii) a de novo predicted peptide sequence.

[0045] Preferably, the qualitative difference (in (i) above) may be the result of a point mutation leading to a different amino acid, or the result of a translocation, deletion, indel, and / or fusion compared to a reference peptide sequence, which is preferably a predicted peptide sequence from DNA sequencing data from a second normal sample of the patient.

[0046] Preferably, the neoantigen comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 amino acids upstream and / or downstream of at least one identified difference, said upstream amino acids being likely to be identical in tumor and non-tumor cells.

[0047] Neoantigens are preferentially sequences at least 7 amino acids long, preferably greater than 10 amino acids long, more preferably greater than 15 amino acids long, even more preferably greater than 20 amino acids long, preferably less than 200 amino acids long, more preferably less than 100 amino acids long, even more preferably less than 50 amino acids long, preferably about 27 amino acids long.

[0048] Preferably, the qualitative difference is located substantially in the center of the sequence of the neoantigen.

[0049] Preferably, the neoantigen may also comprise at least a portion of a novel peptide sequence encoded from an open reading frame, wherein said open reading frame is present only in the tumor, and wherein said at least a portion of the novel peptide sequence is preferentially at least 7 amino acids in length, preferably more than 10 amino acids in length, more preferably more than 15 amino acids in length, even more preferably more than 20 amino acids in length, preferably less than 200 amino acids in length, more preferably less than 100 amino acids in length, even more preferably less than 50 amino acids in length, preferably about 27 amino acids in length.

[0050] Preferably, the neoantigen sequence is converted into a nucleic acid sequence by tools that (i) optimize codon usage for optimal antigen / neoantigen expression in humans, and / or (ii) reduce the generation of secondary structures in the nucleic acid sequence, and / or (iii) avoid RNA motifs associated with undesirable immune responses, and / or (iii) favor motifs associated with desired immune responses.

[0051] Preferably, the neoantigens generated exhibit at least 10% (at least 20, 30, 40, 50, 60, 70, 80, 90%, substantially all) of all identified differences in the encoded peptides.

[0052] Indeed, if the generated neoantigens represent most of the identified differences in the encoded peptides, it is more likely that some of the generated neoantigens, when introduced into a personalized vaccine, will induce a stronger immunological response in the patient, thus increasing the effectiveness of the personalized vaccine.

[0053] Preferably, multiple neoantigens identified according to the invention are incorporated into multiple different constructs, preferably one construct per neoantigen.

[0054] Advantageously, each construct of the plurality of different constructs is a synthetic DNA molecule comprising one segment encoding a tumor neoantigen under the control of a promoter for transcription into a corresponding RNA molecule and a segment for translation of said transcribed RNA molecule into a peptide.

[0055] Preferably, the synthetic DNA molecule further comprises a segment encoding a (peptide) sequence for stabilizing and / or targeting and / or transporting the cell synthetic tumor neoantigen in intracellular and / or cell surface vesicular regions.

[0056] More preferably, the synthetic DNA molecule comprises a segment encoding a (peptide) sequence for addressing the encoded tumor neoantigen to an MHC molecule, and / or a segment encoding a translation enhancer and / or a 3'-UTR and / or a 3' polyA tail segment and / or a 5'-UTR.

[0057] Preferably, the construct serves as a DNA template for in vitro transcription to generate an RNA template that can be used in a patient personalized RNA vaccine.

[0058] Preferably, greater than 20%, greater than 30%, greater than 40%, greater than 50%, greater than 60%, greater than 70%, greater than 80%, greater than 90% of the patient's DNA is sequenced. Indeed, by increasing the proportion of the patient's DNA that is sequenced, the number of DNA variants identified increases, allowing for a comprehensive and / or more complete view of the individualized variant profile of the patient's tumor, thereby allowing for the generation of a more complete view of the neoantigenic profile of the patient's tumor.

[0059] The present invention also relates to a pool of DNA sequences encoding said multiple neoantigens, obtainable by the method according to the invention.

[0060] Preferably, the plurality of neoantigens comprises at least 5, preferably at least 10, more preferably at least 40, even more preferably at least 100 different neoantigens.

[0061] Indeed, in order to induce an appropriate immunological response with a personalized vaccine that contains tumor neoantigens of the patient's tumor, it is advantageous for the vaccine to incorporate most of the neoantigens identified in the patient's tumor.

[0062] The number of tumor neoantigens can vary significantly with tumor type, with some tumors being defined as "cold tumors" with very few mutations and neoantigens, whereas other tumors are defined as "hot tumors" characterized by a very large number, e.g., more than 100 different tumor neoantigens. EXAMPLES

[0063] Example 1: Collection of DNA from tumor samples and preparation of DNA for sequencing In a first step, we harvested DNA from the tumor samples. To this end, the following steps were carried out: - From the paraffin-embedded blocks containing the patient's tumor biopsy, the blocks were cut into 7 μm-thick slides, - staining the first and last slides with hematoxylin and eosin (H&E slides); - Identifying by visual inspection an area of ​​the slide from the first slide that contains sufficient tumor cells of good shape; - On this first stained slide, mark the area containing the most tumor cells; - transfer the markings of the first slide onto an unstained slide, but on a different slice, to mark the same areas of the tumor; - Extraction of cells from these marked areas (macrodissection step), - Extract DNA from these slides and form DNA for sequencing, - In parallel, quantitate DNA - In parallel, the extracted DNA is characterized. The DNA can be used for sequencing.

[0064] Example 2: Harvesting RNA from Tumor Samples and Preparing RNA for Sequencing To obtain RNA from tumor samples, we performed the same steps as described in Example 1, but obtained RNA instead of DNA. The RNA can then be used for sequencing.

[0065] Example 3: Sequencing of DNA from samples isolated from tumors and from PBMCs isolated from blood obtained from patients Whole genome sequencing was performed on the DNA collected in Example 1 and on DNA collected from PBMCs isolated from blood; here, a NovaSeq sequencer was used using a NovaSeq S2 flow cell, and the sequencing performed was "paired-end sequencing": * The average sequencing coverage is at least 50 times. The sequencing made it possible to obtain a passing filter of 90% of the clusters relative to the total number. The sequencing reads are approximately 4 billion (4 billion / 8 samples).

[0066] Example 4: Sequencing of RNA from Tumor Samples Obtained from Patients From the RNA collected in Example 2, the inventors used NEBNext (登録商標) Ultra (商標) Libraries were prepared using the Illumina II Directional RNA Library Prep Kit (NEB). The key steps of this protocol are (i) removal of the ribosomal RNA fraction from 80 ng of total RNA using the QIAseq FastSelect -rRNA HMR Kit (Qiagen), (ii) fragmentation with divalent cations at high temperature to obtain fragments of approximately 300 bp, (iii) double-stranded cDNA synthesis using reverse transcriptase and random primers, and (iv) Illumina adaptor ligation and cDNA library amplification by PCR for sequencing. RNA sequencing was then performed using an Illumina NovaSeq 6000 in paired-end 100b mode.

[0067] Example 5: Identification of DNA variants from patient tumors Using bioinformatics analyses, such as ABRA (https: / / academic.oup.com / bioinformatics / article / 30 / l9 / 2813 / 2422200), SAMTOOLS (https: / / www.ncbi.nlm.nih.gov / pmc / articles / PMC2723002 / ), BEDTOOLS (https: / / academic.oup.com / bioinformatics / article / 26 / 6 / 841Z244688) or FASTP (https: / / academic.oup.com / bioinformatics / article / 34 / 17 / 1884 / 5093234), the sequencing data obtained by whole genome sequencing (see Example 3) made it possible to identify multiple DNA variants in the patient's tumor by comparing the sequencing data of the tumor with the sequencing data of the PBMCs.

[0068] Example 6: Identification of neoantigens in patient tumors The open reading frame in which the mRNA was detected was identified from RNA sequencing (see Example 4). Then, compared with the predicted peptide sequence derived from DNA sequencing data from PBMC, the mRNA with the predicted peptide sequence having at least one different amino acid was selected. Then, the inventors investigated whether the selected mRNA was derived from the DNA variant identified in Example 5. Then, the inventors identified the neoantigen of the tumor by predicting the peptide sequence of the selected mRNA.

[0069] Example 7: Generation of multiple neoantigens from patient tumors The inventors identified differences between the predicted peptide sequences of selected mRNAs (see Example 6) and the corresponding predicted peptide sequences derived from DNA sequencing data from PBMCs. Based on this, 20 neoantigens of 27 amino acids were designed, each having a different amino acid sequence compared to the corresponding sequence of the matched normal tissue. For each neoantigen, the difference is located substantially in the middle of the 27 amino acid amino acid sequence. These 20 neoantigens have amino acid sequences corresponding to SEQ ID NO: 1 to SEQ ID NO: 20. Furthermore, 40 random epitopes (antigens) were designed as internal controls for in vitro testing, having amino acid sequences corresponding to SEQ ID NO: 21 to SEQ ID NO: 60.

[0070] It should be understood that the invention is not limited to the described embodiments, but modifications can be applied without departing from the scope of the claims.

Claims

1. A method for generating a plurality of neoantigens from a sample obtained from a patient, comprising the steps of: - sequencing the DNA and / or RNA from the sample; - identifying a number of DNA and / or RNA variants from the sequencing data; - identifying from said plurality of DNA and / or RNA variants those which cause qualitative differences at the peptide level; and generating the plurality of neoantigens, wherein each neoantigen comprises at least one of the identified differences in the encoded peptide and amino acid(s) upstream and / or downstream of the at least one identified difference to form the neoantigen.

2. 2. The method of claim 1, wherein the samples comprise a first tumor sample comprising DNA and / or RNA derived from at least one tumor cell of the patient, and a second normal sample comprising DNA and / or RNA derived from a blood sample of the patient and / or DNA and / or RNA derived from matched normal tissue of the patient.

3. 3. The method of claim 2, wherein the DNA variants are identified by comparing the sequencing data from the first tumor sample with the sequencing data from the second normal sample.

4. 4. The method of claim 3, wherein more than 20%, more than 30%, more than 40%, more than 50%, more than 60%, more than 70%, more than 80%, or more than 90% of the patient's DNA is sequenced.

5. 2. The method of claim 1, wherein the generated neoantigen exhibits at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90%, or substantially all, of all identified differences in the encoded peptides.

6. 2. The method of claim 1, wherein the identified neoantigens are incorporated into multiple different constructs, or preferably one construct per neoantigen.

7. 2. The method of claim 1, wherein the neoantigen comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 amino acids upstream and / or downstream of the identified at least one difference, and the upstream amino acids may be identical in tumor cells and non-tumor cells.

8. 2. The method of claim 1, wherein the qualitative difference in the encoded peptide is determined by identifying an open reading frame in which mRNA is identified from the first tumor sample, wherein the mRNA(s) have (i) a predicted peptide sequence with at least one different amino acid compared to a reference peptide sequence, and / or (ii) a de novo predicted peptide sequence.

9. The method of claim 1, wherein the patient's total DNA is sequenced.

10. A pool of DNA or RNA sequences encoding said multiple neoantigens obtainable by the method according to any one of claims 1 to 9.

11. 11. The pool of claim 10, wherein the plurality of neoantigens comprises at least 5, preferably at least 10, more preferably at least 40, or even more preferably at least 100 different neoantigens.